{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 一、\t任务说明：\n",
    "- 利用LightGBM/XGboost实现Happy Customer Bank目标客户（贷款成功的客户）识别\n",
    "\n",
    "[Happy Customer Bank目标客户识别](https://discuss.analyticsvidhya.com/t/hackathon-3-x-predict-customer-worth-for-happy-customer-bank/3802)\n",
    "\n",
    "##### 1)\t文件说明\n",
    "- Train.csv：训练数据\n",
    "- Test.csv：测试数据\n",
    "\n",
    "##### 2)\t字段说明\n",
    "数据集共26个字段: 其中1-24列为输入特征，25-26列为输出特征。\n",
    "1.\tID - 唯一ID（不能用于预测）\n",
    "2.\tGender - 性别\n",
    "3.\tCity - 城市\n",
    "4.\tMonthly_Income - 月收入（以卢比为单位）\n",
    "5.\tDOB - 出生日期\n",
    "6.\tLead_Creation_Date - 潜在（贷款）创建日期\n",
    "7.\tLoan_Amount_Applied - 贷款申请请求金额（印度卢比，INR）\n",
    "8.\tLoan_Tenure_Applied - 贷款申请期限（单位为年）\n",
    "9.\tExisting_EMI -现有贷款的EMI（EMI：电子货币机构许可证） \n",
    "10.\tEmployer_Name雇主名称\n",
    "11.\tSalary_Account - 薪资帐户银行\n",
    "12.\tMobile_Verified - 是否移动验证（Y / N）\n",
    "13.\tVAR5 - 连续型变量\n",
    "14.\tVAR1-  类别型变量\n",
    "15.\tLoan_Amount_Submitted - 提交的贷款金额（在看到资格后修改和选择）\n",
    "16.\tLoan_Tenure_Submitted - 提交的贷款期限（单位为年，在看到资格后修改和选择）\n",
    "17.\tInterest_Rate - 提交贷款金额的利率\n",
    "18.\tProcessing_Fee - 提交贷款的处理费（INR）\n",
    "19.\tEMI_Loan_Submitted -提交的EMI贷款金额（INR）\n",
    "20.\tFilled_Form - 后期报价后是否已填写申请表格\n",
    "21.\tDevice_Type - 进行申请的设备（浏览器/移动设备）\n",
    "22.\tVar2 - 类别型变量\n",
    "23.\tSource - 类别型变量\n",
    "24.\tVar4 - 类别型变量\n",
    "\n",
    "输出：\n",
    "25.\tLoggedIn - 是否login（只用于理解问题的变量，不能用于预测，测试集中没有）\n",
    "26. Disbursed - 是否发放贷款（目标变量），1为发放贷款（目标客户）\n",
    "\n",
    "### 二、作业要求：\n",
    "1.\t适当的特征工程（20分）\n",
    "2.\t用LightGBM完成任务，并用交叉验证对模型的超参数（learning_rate、n_estimators、num_leaves、max_depth、min_data_in_leaf、colsample_bytree、subsample）进行调优。（70分）\n",
    "或者用XGBoost完成任务，并用交叉验证对模型的超参数（learning_rate、n_estimators、max_depth、min_child_weight、colsample_bytree、subsample、reg_lambda、reg_）进行调优。\n",
    "3.\t对最终模型给出特征重要性（10分）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "# 计算分类正确率\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2698: DtypeWarning: Columns (12,18) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Monthly_Income</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Lead_Creation_Date</th>\n",
       "      <th>Loan_Amount_Applied</th>\n",
       "      <th>Loan_Tenure_Applied</th>\n",
       "      <th>Existing_EMI</th>\n",
       "      <th>Employer_Name</th>\n",
       "      <th>...</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Processing_Fee</th>\n",
       "      <th>EMI_Loan_Submitted</th>\n",
       "      <th>Filled_Form</th>\n",
       "      <th>Device_Type</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Source</th>\n",
       "      <th>Var4</th>\n",
       "      <th>LoggedIn</th>\n",
       "      <th>Disbursed</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>ID000002C20</td>\n",
       "      <td>Female</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>20000</td>\n",
       "      <td>23-May-78</td>\n",
       "      <td>15-May-15</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CYBOSOL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
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       "      <td>S122</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID000004E40</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>35000</td>\n",
       "      <td>07-Oct-85</td>\n",
       "      <td>04-May-15</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TATA CONSULTANCY SERVICES LTD (TCS)</td>\n",
       "      <td>...</td>\n",
       "      <td>13.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6762.9</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
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       "      <td>S122</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID000007H20</td>\n",
       "      <td>Male</td>\n",
       "      <td>Panchkula</td>\n",
       "      <td>22500</td>\n",
       "      <td>10-Oct-81</td>\n",
       "      <td>19-May-15</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ALCHEMIST HOSPITALS LTD</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID000008I30</td>\n",
       "      <td>Male</td>\n",
       "      <td>Saharsa</td>\n",
       "      <td>35000</td>\n",
       "      <td>30-Nov-87</td>\n",
       "      <td>09-May-15</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BIHAR GOVERNMENT</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID000009J40</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>100000</td>\n",
       "      <td>17-Feb-84</td>\n",
       "      <td>20-May-15</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>GLOBAL EDGE SOFTWARE</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S134</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Gender       City  Monthly_Income        DOB  \\\n",
       "0  ID000002C20  Female      Delhi           20000  23-May-78   \n",
       "1  ID000004E40    Male     Mumbai           35000  07-Oct-85   \n",
       "2  ID000007H20    Male  Panchkula           22500  10-Oct-81   \n",
       "3  ID000008I30    Male    Saharsa           35000  30-Nov-87   \n",
       "4  ID000009J40    Male  Bengaluru          100000  17-Feb-84   \n",
       "\n",
       "  Lead_Creation_Date  Loan_Amount_Applied  Loan_Tenure_Applied  Existing_EMI  \\\n",
       "0          15-May-15             300000.0                  5.0           0.0   \n",
       "1          04-May-15             200000.0                  2.0           0.0   \n",
       "2          19-May-15             600000.0                  4.0           0.0   \n",
       "3          09-May-15            1000000.0                  5.0           0.0   \n",
       "4          20-May-15             500000.0                  2.0       25000.0   \n",
       "\n",
       "                         Employer_Name    ...    Interest_Rate Processing_Fee  \\\n",
       "0                              CYBOSOL    ...              NaN            NaN   \n",
       "1  TATA CONSULTANCY SERVICES LTD (TCS)    ...            13.25            NaN   \n",
       "2              ALCHEMIST HOSPITALS LTD    ...              NaN            NaN   \n",
       "3                     BIHAR GOVERNMENT    ...              NaN            NaN   \n",
       "4                 GLOBAL EDGE SOFTWARE    ...              NaN            NaN   \n",
       "\n",
       "  EMI_Loan_Submitted Filled_Form  Device_Type  Var2  Source  Var4 LoggedIn  \\\n",
       "0                NaN           N  Web-browser     G    S122     1        0   \n",
       "1             6762.9           N  Web-browser     G    S122     3        0   \n",
       "2                NaN           N  Web-browser     B    S143     1        0   \n",
       "3                NaN           N  Web-browser     B    S143     3        0   \n",
       "4                NaN           N  Web-browser     B    S134     3        1   \n",
       "\n",
       "  Disbursed  \n",
       "0       0.0  \n",
       "1       0.0  \n",
       "2       0.0  \n",
       "3       0.0  \n",
       "4       0.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读数据\n",
    "dpath = './data/'\n",
    "train = pd.read_csv(dpath + 'Train.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87020 entries, 0 to 87019\n",
      "Data columns (total 26 columns):\n",
      "ID                       87020 non-null object\n",
      "Gender                   87020 non-null object\n",
      "City                     86017 non-null object\n",
      "Monthly_Income           87020 non-null int64\n",
      "DOB                      87020 non-null object\n",
      "Lead_Creation_Date       87020 non-null object\n",
      "Loan_Amount_Applied      86949 non-null float64\n",
      "Loan_Tenure_Applied      86949 non-null float64\n",
      "Existing_EMI             86949 non-null float64\n",
      "Employer_Name            86949 non-null object\n",
      "Salary_Account           75256 non-null object\n",
      "Mobile_Verified          87020 non-null object\n",
      "Var5                     87020 non-null object\n",
      "Var1                     87019 non-null object\n",
      "Loan_Amount_Submitted    52407 non-null float64\n",
      "Loan_Tenure_Submitted    52407 non-null float64\n",
      "Interest_Rate            27726 non-null float64\n",
      "Processing_Fee           27420 non-null float64\n",
      "EMI_Loan_Submitted       27727 non-null object\n",
      "Filled_Form              87020 non-null object\n",
      "Device_Type              87020 non-null object\n",
      "Var2                     87020 non-null object\n",
      "Source                   87020 non-null object\n",
      "Var4                     87020 non-null int64\n",
      "LoggedIn                 87020 non-null int64\n",
      "Disbursed                87019 non-null float64\n",
      "dtypes: float64(8), int64(3), object(15)\n",
      "memory usage: 17.3+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#删除对结果影响不大的ID、申请日期、申请设备、是否登录\n",
    "train=train.drop(['ID','Lead_Creation_Date','Device_Type','LoggedIn'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "#替换性别\n",
    "train['Gender']=train['Gender'].replace({'Male':0,'Female':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "#替换电话确认\n",
    "train['Mobile_Verified']=train['Mobile_Verified'].replace({'N':0,'Y':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#替换填写表格\n",
    "train['Filled_Form']=train['Filled_Form'].replace({'N':0,'Y':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "#替换DOB为申报时年龄\n",
    "train['DOB']=train['DOB'].apply(lambda s:(2015-(1900+int(s[-2:]))))\n",
    "train=train.rename(columns={'DOB':'Age'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "697"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.City.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43567"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Employer_Name.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "#由于城市City太多，暂时也不知道怎么按照GDP或一二线或省份来分类，时间不足以研究，无奈drop。任职公司Employer_Name同理drop。此外，开户行由个人经验感觉影响不大，也drop。\n",
    "train=train.drop(['City','Employer_Name','Salary_Account'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87020 entries, 0 to 87019\n",
      "Data columns (total 19 columns):\n",
      "Gender                   87020 non-null int64\n",
      "Monthly_Income           87020 non-null int64\n",
      "Age                      87020 non-null int64\n",
      "Loan_Amount_Applied      86949 non-null float64\n",
      "Loan_Tenure_Applied      86949 non-null float64\n",
      "Existing_EMI             86949 non-null float64\n",
      "Mobile_Verified          87020 non-null object\n",
      "Var5                     87020 non-null object\n",
      "Var1                     87019 non-null object\n",
      "Loan_Amount_Submitted    52407 non-null float64\n",
      "Loan_Tenure_Submitted    52407 non-null float64\n",
      "Interest_Rate            27726 non-null float64\n",
      "Processing_Fee           27420 non-null float64\n",
      "EMI_Loan_Submitted       27727 non-null object\n",
      "Filled_Form              87020 non-null object\n",
      "Var2                     87020 non-null object\n",
      "Source                   87020 non-null object\n",
      "Var4                     87020 non-null int64\n",
      "Disbursed                87019 non-null float64\n",
      "dtypes: float64(8), int64(4), object(7)\n",
      "memory usage: 12.6+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>4822</td>\n",
       "      <td>61</td>\n",
       "      <td>271</td>\n",
       "      <td>11</td>\n",
       "      <td>3274</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>19</td>\n",
       "      <td>200</td>\n",
       "      <td>6</td>\n",
       "      <td>66</td>\n",
       "      <td>313</td>\n",
       "      <td>2831</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1989</td>\n",
       "      <td>48</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>1085</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>19</td>\n",
       "      <td>151</td>\n",
       "      <td>5</td>\n",
       "      <td>72</td>\n",
       "      <td>528</td>\n",
       "      <td>5100</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mobile</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Gender  Monthly_Income  Age  Loan_Amount_Applied  \\\n",
       "Filled_Form                                                     \n",
       "0                 2            4822   61                  271   \n",
       "1                 2            1989   48                   72   \n",
       "Mobile            1               1    1                    1   \n",
       "\n",
       "             Loan_Tenure_Applied  Existing_EMI  Mobile_Verified  Var5  Var1  \\\n",
       "Filled_Form                                                                   \n",
       "0                             11          3274                2    37    19   \n",
       "1                              6          1085                2    35    19   \n",
       "Mobile                         1             1                1     1     0   \n",
       "\n",
       "             Loan_Amount_Submitted  Loan_Tenure_Submitted  Interest_Rate  \\\n",
       "Filled_Form                                                                \n",
       "0                              200                      6             66   \n",
       "1                              151                      5             72   \n",
       "Mobile                           0                      0              0   \n",
       "\n",
       "             Processing_Fee  EMI_Loan_Submitted  Filled_Form  Var2  Source  \\\n",
       "Filled_Form                                                                  \n",
       "0                       313                2831            1     7      29   \n",
       "1                       528                5100            1     7      24   \n",
       "Mobile                    0                   1            1     1       1   \n",
       "\n",
       "             Var4  Disbursed  \n",
       "Filled_Form                   \n",
       "0               8          2  \n",
       "1               1          2  \n",
       "Mobile          1          0  "
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filled_Form/Mobile_Verified/EMI_Loan_Submitted应该都已经是数字才对，检查错误\n",
    "train.groupby(by=['Filled_Form']).nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "#居然有一行其值是Mobile，必然有数据错误，删除\n",
    "falseRow=[x for i,x in enumerate(train.index) if train.at[i,'Filled_Form']=='Mobile']\n",
    "train=train.drop(falseRow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经检查，删除的该行包括多处数据错误，删除该行后已经不再有错误字符串。现在需要将这几列的数据转化为数字格式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[['Filled_Form','Mobile_Verified','EMI_Loan_Submitted']] = train[['Filled_Form','Mobile_Verified','EMI_Loan_Submitted']].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 87019 entries, 0 to 87019\n",
      "Data columns (total 19 columns):\n",
      "Gender                   87019 non-null int64\n",
      "Monthly_Income           87019 non-null int64\n",
      "Age                      87019 non-null int64\n",
      "Loan_Amount_Applied      86948 non-null float64\n",
      "Loan_Tenure_Applied      86948 non-null float64\n",
      "Existing_EMI             86948 non-null float64\n",
      "Mobile_Verified          87019 non-null int64\n",
      "Var5                     87019 non-null object\n",
      "Var1                     87019 non-null object\n",
      "Loan_Amount_Submitted    52407 non-null float64\n",
      "Loan_Tenure_Submitted    52407 non-null float64\n",
      "Interest_Rate            27726 non-null float64\n",
      "Processing_Fee           27420 non-null float64\n",
      "EMI_Loan_Submitted       27726 non-null float64\n",
      "Filled_Form              87019 non-null int64\n",
      "Var2                     87019 non-null object\n",
      "Source                   87019 non-null object\n",
      "Var4                     87019 non-null int64\n",
      "Disbursed                87019 non-null float64\n",
      "dtypes: float64(9), int64(6), object(4)\n",
      "memory usage: 13.3+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在还有Var1/Var2/Var5/Source为字符串格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n",
      "7\n",
      "37\n",
      "30\n"
     ]
    }
   ],
   "source": [
    "print(train.Var1.nunique())\n",
    "print(train.Var2.nunique())\n",
    "print(train.Var5.nunique())\n",
    "print(train.Source.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2639d317390>"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xIiLSyDWGZDgD33vzlGhAuDxiBPCCc25LGDwTnxxHJqYfBbwXOr4AzAFWZ4kr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2639d1bad68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#OneHot编码的话会大大增加特征维度，先看看这几个特征影响大不大\n",
    "sns.set_context(\"poster\")\n",
    "sns.countplot(x=\"Source\", hue=\"Disbursed\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x263998dc390>"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+J4Yu7nrgOudciZkNBW4F7gJa4YPiQOfcysQ4Z+MD6mSgPfA6MMQ592oi3UTg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\nUhAUEZHUUhAUEZHU+v992C/jmoCS4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2639a1f09e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Var1\", hue=\"Disbursed\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2639950e128>"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sdytFayJ/ptaQJg/VZWaNV1VVRY8ePSgp8XfKfOrUqRMff/wxPXv2LHQrVoT8r82shVVX\nV1NaWupwawElJSWUlpZSXV1d6FasCHkLziw24OL78rauyqmn1fxcXV1Nu3bt8rZuq61t27Y1XyLM\n0vkrpVkL27Ztm7feWlCbNm184o5l5X91ZmaWSA44MzNLJAecWRG46qqrkIQkSkpKKC8v55vf/CYT\nJ07kgw8+qKlbuXIlknjiiScavW5J3H57nbdxLGpNeb9mKT4qa1Ykdt55Z+bNmwfA2rVreeWVV7jz\nzjv55S9/ybx58xgwYAC77bYbCxYsYJ999ilwt2bFzwFnViRKS0s56KCDan4/6qijOOeccxg8eDCj\nR4/mzTffpKysrFZNoX322Wd06NCh0G2YZeVdlGZFrGvXrvzsZz9j+fLlPPvss1l32c2dO5cBAwbQ\nqVMnysvL+c///E9efPHFWuvZvHkzEyZMoGvXrnTv3p1LLrmk1pmHp59+OgMHDqy1zOLFi5HECy+8\nUDNNEtOnT+f8889nl1124Vvf+hYAf/7znznkkEPo0qULXbp04YADDmD27Nm11nfXXXdRUVFBWVkZ\ne+65JzfddNN273fGjBn06tWLjh07MmzYMFatWtXkz87MW3BmRW7IkCGUlpaycOHC7XZNLlu2jFGj\nRnHeeecxdepUNm7cSGVlJWvWrKlVN23aNA455BBmzZpFZWUlV155JTvvvDMTJ07MuZ+pU6cyZMgQ\nZs6cCcC6desYPnw4I0eOZPLkyYQQeOONN/jkk09qLTNx4kQuueQSBg8eTGVlJVdccQUdO3bk3HPP\nBeCJJ57ghz/8IWeddRYnnHAC//M//8PYsWNz7s8sxQFnVuTKysrYZZdd+PDDD7eb9+qrr9K5c2em\nTZtWM+2YY47Zrq5r167MmjULSQwbNoyqqiqmT5/ORRddRPv27XPq58tf/jIPPPBAze+LFi1i7dq1\n3HbbbXTp0gWAI488smb+unXruPrqq5k8eTKTJk0C4IgjjmDDhg1MmTKFc845hzZt2jBlyhSOOOII\nZsyYAUS7aKuqqrjtttty6s8sxbsozVqBEELW6fvttx/r1q3j9NNP57nnnmPDhg1Z60aMGFFr1P3j\njjuONWvWsHz58px7Ofroo2v93rdvXzp37syYMWN44oknWLt2ba35CxYsYP369YwaNYrq6uqax9Ch\nQ/nwww9599132bp1K5WVlRx//PG1lh01alTO/ZmlOODMitzGjRv517/+xZe+9KXt5u29997MmTOH\n5cuXM2zYMLp3786YMWP46KOPatVlDkbco0cPgFqXIDRW5rrKy8t55pln2LJlCyeeeCK77LILw4cP\n56233gLg448/BuDrX/86bdu2rXkMGTIEgHfeeYePPvqI6urq7dad7T2bNZZ3UZoVufnz51NdXc2g\nQYOyzj/22GM59thj+fTTT3n88cf58Y9/zIQJE5g1a1ZNzerVq2stkwrAXXfdFYD27duzefPmWjVV\nVVVZXy/b/dcGDRrEvHnz2LhxI8899xwXXHABp5xyCgsXLqRbt25AdIytrpDu2LEjpaWl2/WZbbes\nWWN5C86siH3yySdceuml9O3bl+985zv11nbu3Jnvf//7HH/88SxdurTWvLlz59bazfnYY49RXl5O\n3759AfjKV77CypUr2bhxY03Nc889l3O/7du3Z/jw4Zx55pk1PQwaNIgOHTrwwQcfMHDgwO0eO+20\nE23atOHAAw/k97//fa31Pfzwwzn3YJbiLTizIlFdXc3ChQsB+Pe//01lZSV33nknGzZsYN68ebRp\n02a7ZWbMmMGCBQsYNmwYu+++O//85z+ZPXs2p512Wq26Tz75hJNPPpkzzzyTRYsWccstt3DllVfW\nnGBy3HHHMXnyZMaPH8+4ceN4+eWXa51IUp8nn3ySe+65h+OOO45evXrx3nvvMWPGDIYOHQpEJ7hc\nddVVnHfeebz99tscfPDBbNu2jX/84x88//zzNaE2ceJERo4cydlnn82JJ57ISy+9xGOPPdbkz9PM\nAWdWJNauXcugQYOQRJcuXejXrx9jx45lwoQJNbsSM+2///7MnTuXCy+8kDVr1rDbbrsxfvx4rrnm\nmlp1F110EcuWLWP06NGUlJRw/vnnc/nll9fM33fffbn77ruZMmUKjz76aM3ZjIcffniDfffr1w9J\nXH755axevZoePXowfPhwrr/++pqaSy65hN13352bb76ZqVOn0r59e/bee29Gjx5dUzNixAjuuOMO\nrr/+eu677z4OPvhg7r//fg477LAcP0mziOo6O8ta3sCBA8OiRYtqfs/n/cig9j3JdoSWup/ajtJS\n/afObOzYsWPe1m+f8+f7xSKpMoQwsOHKAh+Dk/QVSbdJWiBpg6QgqXeWuvaSpkp6X9Jncf2hWepK\nJF0maaWkjZJek3RiHa89XtLfJW2S9KakH9ZRd5yk/xev721JkyRtv6/IzMyKSqFPMukHfA+oAv5v\nPXV3A+OBycBw4H3gaUkHZNRdC1wF3A4cDSwEZkuqdeWrpPHADOARYBgwG7hD0jkZdUfFNS/H67sV\nmARcj5mZFbVCH4P7UwjhSwCSfgAcmVkg6RvAKcCZIYR742kvAkuAa4AR8bSewE+AG0MIqWEdnpfU\nD7gReCquKwWuA+4PIUxMq9sduFbSXSGELfH0G4E/hxDOSqvrDEySdHMIIfeLiMzMbIco6BZcCKEx\n95kfAWwBHkxbrhqYBRwlqSyefBTQDpiZsfxMYD9JfeLfBwE9stTdD3QHDgaQtAdwQB11bYm26MzM\nrEgVehdlY1QAK0IImWMQLSEKtH5pdZuAZVnqAPqn1QEsbkpdCGEFsCGtzszMilBrCLhuRMfoMq1J\nm596/iRsf1potjqyrLOxdalp3bJMb5CkytSjKcubmVnjtIaAE5DtWobM8YJyqaOO2sbWbT9WkZmZ\nFZXWEHBryL61VJ42P/Vcru0HystWR5Z1dmtkHUDXtPk5CSEMSD2asryZmTVOawi4JUAfSZlXcfYH\nNvP5MbclQBnQN0sdwNK0Ovj8GFtOdfF1eh3T6szMrAi1hoCbS3TW4kmpCfGp/qOBZ0IIm+LJ84gC\nb0zG8mOBxfHJIQALgI/rqFsDvAQQQlgFvFZH3RbgD01/S2Zm1tIKHnCSRkkaBaR22R0dTxsMEEJ4\nlegSgVsk/UDS4USXCPQBrkytJ4SwGrgZuEzShZIOk3QnMBS4PK1uC3AFME7SlLjuGuBMYHIIIf2e\nIZcDgyXNiOsuILrQ+1ZfA2f2uXfeeYdRo0ax884706VLF0444QRWrVrVqGU3btzIxRdfzG677UaH\nDh0YNGgQf/rTn1q4Y/siKPSF3hCNIpLujvj5ReCw+OcziC7OnkJ0/Os1YFgI4ZWMZScCnwI/BnYF\n3gS+F0J4PL0ohPALSQG4CLgYWAWcG0K4I6PuqTh8rwROBz4kGsXkuqa8UbP65Hss0lw1dfzPDRs2\nMHToUMrKyvjNb36DJCZNmsSQIUN4/fXX6dSpU73L/9d//RdPPvkkU6dOZa+99uLnP/85Rx11FAsW\nLOCAAzIHKzJrvIIHXAihwTMSQwifARfGj/rqthKF4JRGrHMG0XBdDdU9CjzaUJ3ZF9WvfvUr3nrr\nLd5880369YsuS91///356le/yowZM7jwwrr/2b722ms88MAD3HPPPZxxxhkADB48mIqKCiZPnszc\nuXN3yHuwZCr4Lkoza93mzp3LQQcdVBNuAH369OHb3/42c+bMaXDZtm3b1rptTmlpKSeffDJPP/00\nmzZtqmdps/o54MysWZYsWcK+++673fSKiort7iyebdk+ffpsd6ubiooKNm/ezLJlmQMTmTWeA87M\nmmXNmjWUl5dvN71bt25UVWUbCKhxy6bmmzWVA87Mmm378RWgMTdTDiE0eVmzhjjgzKxZysvLs25p\nVVVVZd06S9etW7c6l03NN2sqB5yZNUtFRQVLlizZbvrSpUvp37/+m25UVFSwYsUKNmyofbOQpUuX\n0q5du1onrpjlygFnZs0yYsQIFi5cyFtvvVUzbeXKlbz00kuMGDGiwWW3bNnC7NmfXw5bXV3Ngw8+\nyJFHHklZWVk9S5vVzwFnZs0yfvx4evfuzciRI5kzZw5z585l5MiR7LHHHpx99tk1dW+//TalpaVc\nc801NdMOOOAARo8ezfnnn89dd93FH//4R04++WRWrFjB1VdfXYi3YwnigDOzZunUqRPz58/na1/7\nGqeeeipjxoyhT58+zJ8/n86dO9fUhRDYunUr27Ztq7X8vffeyxlnnMGkSZM49thjeeedd5g3bx4H\nHnjgjn4rljAFH8nEzCJNHSqrGPTq1YtHHnmk3prevXtnPTuyQ4cOTJ8+nenTp7dUe/YF5S04MzNL\nJAecmZklkgPOzMwSyQFnZmaJ5IAzM7NEcsCZmVkiOeDMzCyRHHBmZpZIDjgzM0skB5yZmSWSA87M\nmuXdd99lwoQJDBo0iI4dOyKJlStXNmrZbdu2ccMNN9C7d2/at2/PN77xjQaH/DJrLI9FaVYkVl2z\nX0Ffv9fkN5q03LJly3jooYcYMGAAhxxyCM8880yjl73iiiuYNm0a1113HQMGDGDWrFmcdNJJPPHE\nExxzzDFN6scsxQFnZs1y6KGH8uGHHwJw1113NTrgVq9ezbRp07j00kv5yU9+AsCQIUNYtmwZl156\nqQPOms27KM2sWUpKmvbfyNNPP83mzZsZO3Zsreljx47ljTfeYMWKFfloz77AHHBmVhBLliyhrKyM\nfv361ZpeUVEBwNKlSwvRliWIA87MCmLNmjV07doVSbWmd+vWrWa+WXM44MysIEII24VbarpZPjjg\nzKwgunXrRlVV1XaBVlVVVTPfrDkccGZWEBUVFWzatInly5fXmp469ta/f/9CtGUJ4oAzs4IYNmwY\n7dq147e//W2t6TNnzmTfffelT58+BerMksLXwZlZsz388MMAVFZWAvCHP/yBHj160KNHDwYPHgxA\naWkp48aN4+677wagZ8+eXHDBBdxwww3stNNOHHjggTz44IPMnz+fOXPmFOaNWKI44Mys2U466aRa\nv//oRz8CYPDgwbzwwgsAbN26la1bt9aqu+666+jcuTO33norH3zwAXvvvTcPPfQQ3/3ud3dI35Zs\nDjizItHUobKKQWPOfMxW06ZNGyZNmsSkSZNaoi37gvMxODMzSyQHnJmZJZIDzszMEskBZ2ZmieSA\nMzOzRHLAmbWwkpKS7U6Pt/zZunUrbdq0KXQbVoQccGYtrF27dqxfv96DCLeAEALr16+nbdu2hW7F\nipCvgzNrYSUlJXTv3p0PP/yQTp06eWsjT7Zu3cr69evp3r17k2+6asnmgDPbAdq2bUvPnj3ZsmWL\nd1fmSbt27ejUqZPDzerkgDPbQUpKSigrK2ux9Q+4+L68raty6ml5W5dZofirTwMk7SHpYUlrJa2T\n9KikXoXuy8zM6ueAq4ekjsB8YB9gHHAq8FXgeUmdCtmbmZnVz7so6zce2AvYO4SwDEDS68A/gbOB\n6QXszczM6uGAq98IYGEq3ABCCCskvQSMxAHXYlZds19e19eaR+r/omjtxxBbe/9J5ICrXwWQ7c6L\nS4CTskwvKvkMCQeEmbU2Drj6dQOqskxfA5Q3ZYWSKlM/DxgwoIltWbHzlwuzwpNHV6ibpM3Af4cQ\nLsuYfh3w0xBCzl8Q0gMO6AW83bwu6/T1+PlvLbT+ltSaewf3X2juv7Bauv89Qwg9GlPoLbj6VRFt\nxWUqJ/uWXYNCCDtksy0VpCGEgTvi9fKpNfcO7r/Q3H9hFVP/vkygfkuIjsNl6g8s3cG9mJlZDhxw\n9ZsLHCRpr9QESb2Bb8fzzMysSPkYXD3ii7lfAz4DJgEBuBbYCdg/hPBpAdszM7N6eAuuHiGE9cBQ\n4B/A/cBvgRXAUIebmVlx8xacmZklkrfgzMwskRxwZmaWSA44MzNLJAecmZklkgPOzMwSyQFnZmaJ\n5IAzM7NEcsCZmVkiOeASRtIgSbMkvStps6R1kl6WdK2k3QrdX30knS4ppD22SvpfSQ9J2rvQ/TVW\n/GfwkKT34j+Df0l6VtI4SW0K3V9dsnz+6Y9PCt1fQxro/zuF7q8uWfpeL2mlpN9L+p6kVvP/tKS7\n4vcwvdC9gG+XkyiSLgKmAs8TjZ35FtAZ+BZwFjAQOLpgDTbeScC7QBugL3AF8EdJFSGEtQXtrAGS\nzgemA/OBnxLd768cOBK4E/jiUwXxAAAHBElEQVSE7HeJLyapzz9ddSEaaaJs/beGu3+k+i4julfk\nscDvgLMkfTeE8Fkhm2uIpA5E7wFgjKRLQggF/XvjgEsISUOIwu3WEMIFGbOfknQDn//lK3avhhCW\nxT+/JOk94FmioP5D4dqqn6RDicLt9hDCeRmz58Tfajvt+M5ylv75t0attf/Mvu+XNBuYDdwETChM\nW412PNAFeAo4BhgGPFHIhlrNpq816KfAx/HzdkII60MIv96hHeXPuvi5bUG7aNilwBrgkmwzQwjL\nQwiv79iWrDULITxCtMU/XlLHQvfTgHFEN4I+negOLKcVtBsccIkgqRQYDDwbQthc6H7yoI2kUkll\nkr4OXA+sBl4obFt1i4+tHQY8E0LYWOB2miv1+ac/WtP/FZn9F+1xz0Z6imi3ZcHvkF0XSbsD3wEe\nDCF8BDwGjJBUXsi+WtNfWqtbd6A9sCpzRuZ/VDu+tSb5O7AF2Eh07OTrwPAQwrp6lyqsXYAORMfc\nWrvU55/+aE03+M3s/8XCttNsqX/XxXyS2KlEeXJf/PtviEJ5dME6wsfgkkJZJ0q7Au9nTGtb6AO/\njXA80cF2AbsD5xIdRzw0hPC3gnb2xZD6/NMV/VmUaTL7/3ehGsmT1L/vYr632WnAP0MIC+LfnwPe\ni6f/olBNOeCS4WOirZ1eWaZ/M/75LGD8jmyqGRanH2yX9AzwDnAVBf5GWI9/ER132LPQjeTB4lZ6\nkkZKa+8/0x7x8/v1VhWIpG8C/YGfSeqaNutR4FxJXwsh/KMQvXkXZQLEW2R/Ao6Q1C59eghhUQhh\nEdG3qVYpPj36LWD/QvdSl/jP4AWiP4OyArdjyXIs0RfYykI3Uodx8fNPiU4yST3OjacX7GQTB1xy\n3ER0HOhnhW4k3+Kzx/oCHxW6lwbcSHQ8dGq2mZL6SCrakLbiI+kEYATwixDChkL3kyn+Qn0y8Bdg\nSJbHq8CpkrIeRmlp3kWZECGEP0q6FLgx/k/0PmAF0cknXyP6S7ie4t6Pn3KApF2Ijj3sRvRNsBtw\nW0G7akAI4U+SLgSmx2d//proBIFy4HDgB8ApQLFfKpD6/DMtagXHb1uz1Ofejuhww3Cia1efBS4r\nZGP1GE70pe6iEMILmTMlzSAa4OAwogEodigHXIKEEG6S9BLwY6JT63sQ7dp4E3iQ6Fvg1gK22Fiz\n037+CFgMDAshPF2gfhothHCLpL8CFwDTiLaq/w0sAs4GHi9ge401u47pPYiO61rLSH3uG4kui3mF\n6IvpwyGEYv1iOo7o73ddf2d+RzT4wTgKEHAq3s/NzMys6XwMzszMEskBZ2ZmieSAMzOzRHLAmZlZ\nIjngzMwskRxwZmaWSA44MzNLJAecWQJIelhSkNSvnprdJW2VlLeLzSXtLek6SX+V9C9JGyUtlvRT\nScV+g1pLOAecWTL8On6ub2Db1D27fl1PTa7GA+cRjZYzGbiI6M4PNwKPFWoMQjPwSCZmiRDftfpd\nYBPQJ9vQTpKWAj2B3Zt753dJnUII6+NbpbyZeTNaSQ8A36eVDLFmyeQtOLMEiMcYnUl0P7rBmfMl\n/QfRndEfCCFslvQVSbdIekPSOknrJf1F0qgsy86Md3/uIek+SR8T3wA1hPByHXdaT41NuF++3qNZ\nrhxwZsnx6/h5XJZ5p2fUHAAcA/wBuJjoZrIdgdmSvl/H+p8EdibaFXlNA73sHj8X+y2OLMG8i9Is\nQSS9DOwD7BpCWB9PKyO6G/S7IYT942kdgI3puzLjaa8Cm0MI+6VNnwmMAX4dQjijET10At4gusXR\nXiGENfl6f2a58BacWbLcC3QGTkibNoLonnT3piaEED5LhZuk9pK6A52I7gxfEd9kNtPPG3pxSSVE\n9yLsA5zncLNCcsCZJcvviE40Sd9NOQ6oBn6bmiCpnaQpkt4GPiO6z9tHRDdlFdA1y7qXN+L17yQK\n16tDCPc16R2Y5YlveGqWICGEKklzgRMl7QFsBo4CngohrE4rvQ04C/gN0R2jPwa2EgXcaLJ/+f2s\nvteWdGu8zptCCFc1862YNZsDzix57gVOIrrubRPRv/N7M2pOAf4YQjg9faKks5rygpKmEV0Pd0sI\n4adNWYdZvjngzJLnGeA9ol2Tm4i2zp7MqKkm2hVZQ9LeRMfrciLpBqILvO8IIVzQlIbNWoIDzixh\nQghb4zMfL4kn3RpC2JJR9hhwuqT7iU4s6Q38CPgb0SUEjSLpAuBSYBmwUNLYjJJXQwiLc38XZs3n\ngDNLpnv5POB+nWX+ecAG4HhgFFGwjScKt0YHHDAgfu5HdPZkpisAB5wVhK+DMzOzRPJlAmZmlkgO\nODMzSyQHnJmZJZIDzszMEskBZ2ZmieSAMzOzRHLAmZlZIjngzMwskRxwZmaWSA44MzNLpP8PkuNE\nA5JntjYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x263995890f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Var2\", hue=\"Disbursed\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2639960df28>"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iIpJoSnQiIpJoSnQiIpJoSnQiIpJoSnQiIpJoOutS6oSeQi4iDYV6dCIikmhK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\n9v8BZiE5BjdYJXcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2639996b9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Var5\", hue=\"Disbursed\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "#影响不是很大，所以也先drop了……\n",
    "train=train.drop(['Var1','Var2','Var5','Source'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x263996d81d0>"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ov1jey2s9Pj6/4+d3iPPnD+HrewQvba5u3TrStGlD3n//XW7fDs57VKhQ3uxc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\nfQ2v7b79u7NssSc3b97m1q07LF28hn4Dnl8F1Ji2bu+2Z+b0BWg0Ws6cPo/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mOMMcYYY0yuYj0xxhhjjDHG/E0cKhP7rSfGGGOMMcYYk6tYT4wxxhhjjDF/EzYnxhhjjDHG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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26399692a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看是否有相关特征可以删除\n",
    "sns.set_context(\"notebook\")\n",
    "data_corr = train.corr().abs()\n",
    "pyplot.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True,fmt='.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，手续费和贷款金额有较大相关性。Loan_Amount_Submitted 和EMI_Loan_Submitted有非常强的相关性，可见贷款主要方式都是EMI，不如将EMI的数据删除。性别和手机号确认有很强相关性，这一点暂时不知原因。\n",
    "表中的空白区域为大量缺失数据的区域。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender                       0\n",
      "Monthly_Income               0\n",
      "Age                          0\n",
      "Loan_Amount_Applied         71\n",
      "Loan_Tenure_Applied         71\n",
      "Existing_EMI                71\n",
      "Mobile_Verified              0\n",
      "Loan_Amount_Submitted    34612\n",
      "Loan_Tenure_Submitted    34612\n",
      "Interest_Rate            59293\n",
      "Processing_Fee           59599\n",
      "Filled_Form                  0\n",
      "Var4                         0\n",
      "Disbursed                    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "train=train.drop('EMI_Loan_Submitted',axis=1)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2639a18f550>"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I2hPYKSIeACZQOIy2fxveey5whKTGUd3YNqxr7eARjLXVI5Lqi+b/G3BjOpn8\nFnBORGyUNAUYLul5Cucd/p32f5o+H7gnjQAAiIgVkv4EnNPGbX0J+KmkRRQ+0U6I9x4vcFla9iqF\n8zjl+iHwqKQNwJCIeL1En57ADZL2ovDpugH4XESsApD0QwqHtF6iMMo5qGjdZ4BRkm5I642mY+ZQ\nOHz5LPC7iPhS030Avgx8H3hWUgDvpLaX0vvfLukrqea573+L0iLiTeDaVrpVA3ekCyY2UXgk9wTg\nw8CtKYBqgNnpvfct873/Juki4LfpooD7KZzDWl9u/dY2vl2/mW03JO0cEevS9PnAuIg4rsJldVse\nwZjZ9uRLkkZQ+N23Bvh8hevp1jyCsS5D0ngKT8dsamzRlVJdmqRbKFytVKw+IgZVop6tSdJM3n+4\nanlEDKtEPVZ5DhgzM8vCV5GZmVkWDhgzM8vCAWNmZlk4YMzMLIv/D4OtSvDNADqCAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2639a182da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#缺失值比较多的，干脆就开一个新的字段，表明是缺失值还是不是缺失值\n",
    "train['Loan_Amount_Submitted_Missing'] = train['Loan_Amount_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "sns.countplot(x=\"Loan_Amount_Submitted_Missing\", hue=\"Disbursed\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2639c925208>"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gfmAx8H3g0pL2N4BnmltR0t7A2ylcdgM+QnHi/iHgbIpzJqOB6WmVGWn+ibT8lxERkmYA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\nFg4YMzPL4n8A/Jc8wYkv1qsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2639984dcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Loan_Tenure_Submitted_Missing'] = train['Loan_Tenure_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "sns.countplot(x=\"Loan_Tenure_Submitted_Missing\", hue=\"Disbursed\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2639df6d828>"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WzMzM6pV6BDOwgdqhbdkRMzPrXpp84JikrwGTgE9Jeq5o0d7AKzk7ZmZmXVtzT7R8DKgC\nbgIuK6r/GViWq1NmZtb1NRkwEfFH4I/Ake3THTMz6y6aO4IBQNKnge8ChxSvExHHZeqXmZl1cSUF\nDDALeAC4A6jL1x0zM+suSg2Ysoj416w9MTOzbqXUy5SflvQfs/bEzMy6lVKPYI4Hzpf0CvBefdFj\nMGZm1phSA+abWXthZmbdTkkBExG/zt0RMzPrXkq9F9liSc/t+Gpmnd1TuxclvSzpe6l+kKRnJVVJ\nuk9Sj1TfLc2vSssHFG3rylR/RdIZRfURqbZK0pSW/ADMzCyPUk+R/dei6d2BsUBNM+u8D5waEe9K\n+hjwlKT5wLeA6yNilqTbgInArel9Y0QMlDQG+BHw95IOB8YARwB9gcclfSp9xs3A6UA1sFjS3IhY\nUeI+mZlZRi06RSbpMQq3kWlqnQDeTbMfS68ATgX+c6rPAKZRCJhRaRpgNnCTJKX6rIh4H3hV0iqg\n/uKCVRGxOvVpVmrrgDEz6wRa+jyYvYCDm2skqVzS74F1wALg/wJvR0RtalIN9EvT/YDXAdLyd4BP\nFtd3WKexenN9miYpJEVNTXMHYWZm1lItGYNZArwKTG9uvYioi4hjgEoKRx2HNdSs/mMaWbaz9eb6\nNC0iFBHq27dvc83NzKyFWjIGUwu8GhEl//c/It6WtAgYCuwjqSIdpVTy0VhONdAfqJZUQeGRABuK\n6vWK12msbmZmHaykI5g0BvNbYD2wkcIpryZJ2lfSPml6D+ALwErgSeDLqdl4YE6anpvmScufSOM4\nc4Ex6Sqzg4BBwHPAYmBQuiqtB4ULAeaWsj9mZpZfqXdTHgL8gsKVYQIqJH0pIp5vYrUDgBmSyikE\n2f0R8bCkFcAsSd8HXgBuT+1vB+5Kg/gbKAQGEfFyejzzCgpHTxdHRF3q1zeAR4FyYHpEvLwT+25m\nZhmVeorsJ8D5EfEEgKRTgP8OnNjYChGxDPhMA/XVfHQVWHH9PWB0I9v6AfCDBurzgHml7YKZmbWn\nUq8i+3h9uABExJPAx/N0yczMuoNSA2ZzOmoBQNLngc15umRmZt1BqafI/gn4haT3KVwKvBvwpWy9\nMjOzLq/UgNkH+CywH4VB/jeBI3N1yszMur5SA+Y6YHBErAOQVAb8GzA4V8fMzKxrKzVglL6TAkBE\nbEuXH+/yjr1sZkd3odNYet24ju6CmXUipQ7yb5J0fP1Mmv5Lni6ZmVl3UOoRzOXALyXVf5HxcODv\n8nTJzMy6g1Jv1/90ei7L31IY5P9dRGzM2jMzM+vSSj2CIQWKvzVvZmYlaenzYMzMzJrkgDEzsywc\nMGZmloUDxszMsnDAmJlZFg4YMzPLwgFjZmZZOGDMzCwLB4yZmWXhgDEzsywcMGZmloUDxszMsnDA\nmJlZFg4YMzPLwgFjZmZZOGDMzCyLbAEjqb+kJyWtlPSypEtSvbekBZKq0nuvVJekGyWtkrRM0uCi\nbY1P7askjS+qHytpeVrnRknKtT9mZrZzch7B1ALfjojDgKHAxemxy1OAhRExCFiY5gHOBAal1yTg\nVigEEjAVOB44DphaH0qpzaSi9UZk3B8zM9sJ2QImIt6IiOfT9CZgJdAPGAXMSM1mAOem6VHAzCh4\nBthH0gHAGcCCiNiQHtu8ABiRlu0VEU9HRAAzi7ZlZmYdrF3GYCQNAD4DPAvsHxFvQCGEgP1Ss37A\n60WrVadaU/XqBupmZtYJZA8YSZ8AfgF8MyL+3FTTBmrRgnpz/ZkmKSRFTU1Nc83NzKyFsgaMpI9R\nCJd7IuJ/pfKb6fQW6X1dqlcD/YtWrwRqmqlXNlBvUkRMiwhFhPr27bvzO2VmZiXJeRWZgNuBlRHx\n70WL5gL1V4KNB+YU1celq8mGAu+kU2iPAsMl9UqD+8OBR9OyTZKGps8aV7QtMzPrYBUZt30icB6w\nXNLvU+07wLXA/ZImAmuA0WnZPOAsYBWwGTgfICI2SLoGWJzaXR0RG9L0ZOBOYA9gfnqZmVknkC1g\nIuIpGh4nATitgfYBXNzItqYD0xuoLwGObEU3zcwsE3+T38zMsnDAmJlZFg4YMzPLwgFjZmZZOGDM\nzCwLB4yZmWXhgDEzsywcMGZmloUDxszMsnDAmJlZFg4YMzPLwgFjZmZZOGDMzCwLB4yZmWXhgDEz\nsywcMGZmloUDxszMsnDAmJlZFg4YMzPLwgFjZmZZOGDMzCwLB4yZmWXhgDEzsywcMGZmloUDxszM\nsnDAmJlZFg4YMzPLIlvASJouaZ2kl4pqvSUtkFSV3nuluiTdKGmVpGWSBhetMz61r5I0vqh+rKTl\naZ0bJSnXvpiZ2c7LeQRzJzBih9oUYGFEDAIWpnmAM4FB6TUJuBUKgQRMBY4HjgOm1odSajOpaL0d\nP8vMzDpQtoCJiN8AG3YojwJmpOkZwLlF9ZlR8Aywj6QDgDOABRGxISI2AguAEWnZXhHxdEQEMLNo\nW2Zm1gm09xjM/hHxBkB63y/V+wGvF7WrTrWm6tUN1JslaZqkkBQ1NTUt2gkzM2teZxnkb2j8JFpQ\nb1ZETIsIRYT69u27E100M7Od0d4B82Y6vUV6X5fq1UD/onaVQE0z9coG6mZm1km0d8DMBeqvBBsP\nzCmqj0tXkw0F3kmn0B4FhkvqlQb3hwOPpmWbJA1NV4+NK9qWmZl1AhW5NizpXmAY0EdSNYWrwa4F\n7pc0EVgDjE7N5wFnAauAzcD5ABGxQdI1wOLU7uqIqL9wYDKFK9X2AOanl5mZdRLZAiYixjay6LQG\n2gZwcSPbmQ5Mb6C+BDiyNX00M7N8Ossgv5mZdTMOGDMzy8IBY2ZmWThgzMwsCweMmZll4YAxM7Ms\nHDBmZpaFA8bMzLJwwJiZWRYOGDMzy8IBY2ZmWThgzMwsCweMmZll4YAxM7MsHDBmZpaFA8bMzLJw\nwJiZWRYOGDMzy8IBY2ZmWThgzMwsCweMmZll4YAxM7MsHDBmZpaFA8bMzLJwwJiZWRYOGDMzy6LL\nB4ykEZJekbRK0pSO7o+ZmRVUdHQHWkNSOXAzcDpQDSyWNDciVnRsz3ZNa64+qqO70GkceNXyju6C\nFfHv5kfa83ezqx/BHAesiojVEbEVmAWM6uA+mZkZXfwIBugHvF40Xw0c39QKkqYBU9PsZkkr83Rt\n1/M30Beo6eh+dApT1dE9sCL+3SzSNr+bf1NKo64eMA39pKKpFSJiGjAtR2d2dZIiIvp2dD/MduTf\nzY7R1U+RVQP9i+Yr8f9SzMw6ha4eMIuBQZIOktQDGAPM7eA+mZkZXfwUWUTUSvoG8ChQDkyPiJc7\nuFu7su91dAfMGuHfzQ6giCaHLMzMzFqkq58iMzOzTsoBY2ZmWThgzMwsCweMmZll4YAxM7MsHDBm\nZpaFA8ZazY9MsM5K0nRJ6yS91NF92RU5YKxVih6ZcCZwODBW0uEd2yuzD90JjOjoTuyqHDDWWn5k\ngnVaEfEbYENH92NX5YCx1mrokQn9OqgvZtaJOGCstXb6kQlmtmtwwFhr+ZEJZtYgB4y1lh+ZYGYN\ncsBYq0RELVD/yISVwP1+ZIJ1FpLuBZ4GPi2pWtLEju7TrsS36zczsyx8BGNmZlk4YMzMLAsHjJmZ\nZeGAMTOzLBwwZmaWhQPGzMyycMBYlyHpNUlHNtNmgqRPtWOfBkiaVEK7YZI2S/q9pJck/VrSoSV+\nxrT0JdbW9DEkPbBDfUaqH5nmfy7pcy3Yfl9JT7a0f9Z9OWCsu5kA7HTASCqT1NB91ZozAGg2YJIV\nEXFMRBwJPANcX+J6U4EWB0yyAThKUi8ASZ8ATgTW1jeIiAsj4n/v7IYjoiYiTmll/6wbcsBYlyNp\nkaTrJD0labWka1P9fGAIcGM6UvhCql8u6TlJz0t6SNJ/SPVpku6W9EvgRWAfSZ+WNF/SYkkvpm0i\nqaekByStSPX7U3duBg5Pnzd7J3ZjEXBg0T59O33mC5KelnRMqt+cmvwufcY+kvZKRxvPSVom6Sfp\nuTxNCeB+YGyaHw38Eqjd4ef6xTQ9SdLK9JnLJB2aQvgWSX9IP4PfprYDJK0v2k5I+k7an9WSvlS0\n7Etp/RdSm0hhZ91RRPjlV5d4Aa8BR1L443wfhf8g7Q2sBwalNouALxat81Xgp0BZmp8M3JOmpwFr\ngD5pvgJYChya5vcEXgEOBf4T8HjRdnul92HAkhL6/mG71O/bgH8pWr5v0fQXgGeK5gP4RNH8z4Hz\nirZ1L/C1Jj57QPoZHVy/3fRzOrL+Z7rjzw54B+ifpncDegKfAf5P0c+yV/H2d+jvN9L0icDaNL0f\n8FbRv9WlO+6bX93rVfHXkWPWJTwQEduAdyStBA4BqhpoN5LCUc3z6QxYBYU/nvXmRUT9/74/BRwG\nzCo6W7Zbqr0IHJqOKBYBv2pBnw+X9HsKz8t5CzihaNmxkr4D9Aa20fRpvpHAcZK+neZ7UrirdZMi\nYrWk9yWdBfSMiJeaOCv4BHCHpDnAr9K6q4Fy4HZJTwAPN/Fxs9L7M0BfSbsDQ4HnI6L+32k68O/N\n9du6LgeMdVXvFU3X0fjvsoDvR8T0Rpa/u0Pb9RFxTIMbkg4DTqPweOh/lXTUznWZFRExJA3YzwJu\nBf4+zc8GTo6I5yX1pWhspKGuAOdGxOqd/HyAGcBMCkdvTfk74LPAqcCTki6KiPmSjqBwNHYa8CNJ\ngxtZ/z2AiKgrCnbhZwXtUjwGY93NnymcNqs3F/iHosHt3SQd3ci6rwCbJZ1XX0hjD3tJqgTqIuKX\nFE7t7EvhaGPHz2tWFB4tPRk4M4217E7hD3D9k0H/YYdVNjWwT1Pqx10k9ZF0UIkffz/wb8A9jTWQ\nVAEcHBHPRcS1wGPAZyTtC+wREY8AUygcCR5c4udC4WjmWEkD0/yEnVjXuiAHjHU3PwX+JQ0ifyEi\n7qLwx/TXkpZRGGM5saEVo/DogXOAMWlg+2XgFgpXcB0FPC3pReA54IcRUQMsA15R4dLjkgf5I+JN\nCn/op0bEn4GrgMWSfgP8ZYfm/w14on6QH/gmhaO2FyUtBx6hxMdUR8S7EXFtRGxsolk5cKek5Wl/\nDwD+B4UHyz2easuA+RRCoyRpny8CfpUuENgD+ADYXOo2rGvx7frNrN1I2jMiNqXp84GJEXFSB3fL\nMvEYjJm1p3+SNJrC354NwNc6uD+WkY9gzNqQpCX89X/cnomIi9rhs+dS9N2aZE1EjMz92WYNccCY\nmVkWHuQ3M7MsHDBmZpaFA8bMzLJwwJiZWRb/H8dEiMgoIm03AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2639a15df60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Interest_Rate_Missing'] = train['Interest_Rate'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "sns.countplot(x=\"Interest_Rate_Missing\", hue=\"Disbursed\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2639c6b5c50>"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DxszMsnDAmJlZFg4YMzPLoqSASbfLb7FmZmbWoNQjmN6N1A5uz46YmVnn0uzzYCR9DbgA\n+HtJ84oW7Q68nLNjZma2fWvpgWOPADXAL4HLiup/BRbl6pSZmW3/mg2YiPgz8Gfg8G3THTMz6yxK\nemSypM8APwAOKl4nIj726GMzMzMoMWCAScD9wO1Afb7umJlZZ1FqwFRExH9m7YmZmXUqpQ5TfkrS\n/8zaEzMz61RKPYLpD4yW9DKwoaHoazBmZtaUUgPmW1l7YWZmnU5JARMR/y93R8zMrHMpdZjyfCC2\nrPsUmZmZNaXUi/zfofCX/JcB/w4sAWY1t4KknSXNk/S8pBclXZXqB0h6WlKNpHsldUn1ndL80rS8\nV9G2rkj1lyWdVlQflGpLJY3dmh03M7O8WnWKTNIjFG4j05z3gZMj4j1JnwAelzQD+DZwfURMknQr\nMAa4Jb2viYjekoYBPwH+SdKhwDDgMKAHMEvS36fPuAk4FagF5kuaFhFLStknMzPLq7XPg9kNOLC5\nBlHwXpr9RHoFcDIwOdUnAGen6SFpnrT8FElK9UkR8X5EvAIsBY5Nr6URsSwiNlL4Y9AhrdwfMzNr\nZ6U+D2Z+Ot01T9IC4BVgfAnrVUp6DlgJzAT+BLwTEXWpSS2wX5reD3gdIC1/F/h0cX2LdZqqt9Sn\ncZJCUqxYsaKl5mZm1kqlDlP+TtF0HfBKRLT42zki6oGjJO0BTAEOaaxZelcTy5qqNxaOHxuI0Eif\nxgHjAPr169diezMza52Sr8FIqgI+Q+GX+Mqt+ZCIeEfSHGAAsIekqnSUUg00BFUt0BOoTZ+1O7C6\nqN6geJ2m6mZmVmalniLrR+H01hRgKlAjqW8L6+yVjlyQtAvwD8BLwGPAOanZyLQ9gGlpnrT80YiI\nVB+WRpkdAPQB5gHzgT5pVFoXCgMBppWyP2Zmll+pp8h+AYyOiEcBJJ0E/BdwfDPr7AtMkFRJIcju\ni4gHJS0BJkn6D+BZ4LbU/jbgTklLKRy5DAOIiBcl3UdhaHQdcHE69YakbwAPA5XA+Ih4scT9MTOz\nzEoNmE82hAtARDwm6ZPNrRARi4CjG6kvozACbMv6BmBoE9v6EfCjRurTgekt9t7MzLa5Uocpr0tH\nLQBI+iKwLk+XzMysMyj1COabwG8lvU/hIv9OwJez9crMzLZ7pQbMHsBngb0pDBt+Ezg8V6fMzGz7\nV2rAXAf0jYiVAJIqgJ8BzY4kMzOzHVep12CUhgwDEBGbKIzcMjMza1SpRzBrJfWPiKcBJPUH/pav\nW9uPYy6bWO4udBgLrxtR7i6YWQdSasB8F/idpIa/MzkU+Mc8XTIzs86g1FvFPJVum/85Chf5n4yI\nNVl7ZmZm27VSj2BIgeI/ajQzs5K09nkwZmZmzXLAmJlZFg4YMzPLwgFjZmZZOGDMzCwLB4yZmWXh\ngDEzsywcMGZmloUDxszMsnDAmJlZFg4YMzPLwgFjZmZZOGDMzCwLB4yZmWXhgDEzsywcMGZmloUD\nxszMssgWMJJ6SnpM0kuSXpR0Sap3lzRTUk1675bqknSjpKWSFknqW7Stkal9jaSRRfVjJC1O69wo\nSbn2x8zMtk7OI5g64N8i4hBgAHCxpEOBscDsiOgDzE7zAKcDfdLrAuAWKAQScCXQHzgWuLIhlFKb\nC4rWG5Rxf8zMbCtkC5iIeCMinknTa4GXgP2AIcCE1GwCcHaaHgJMjIK5wB6S9gVOA2ZGxOqIWAPM\nBAalZbtFxFMREcDEom2ZmVmZbZNrMJJ6AUcDTwP7RMQbUAghYO/UbD/g9aLValOtuXptI/WW+jJO\nUkiKFStWtGZ3zMysBNkDRtKngN8C34qIvzbXtJFatKLerIgYFxGKCPXo0aOl5mZm1kpZA0bSJyiE\ny90R8X9T+c10eov0vjLVa4GeRatXAytaqFc3Ujczsw4g5ygyAbcBL0XEz4sWTQMaRoKNBKYW1Uek\n0WQDgHfTKbSHgYGSuqWL+wOBh9OytZIGpM8aUbQtMzMrs6qM2z4eOBdYLOm5VPsecC1wn6QxwGvA\n0LRsOnAGsBRYB4wGiIjVkq4B5qd2V0fE6jR9EXAHsAswI73MzKwDyBYwEfE4jV8nATilkfYBXNzE\ntsYD4xupLwAOb0M3zcwsE/8lv5mZZeGAMTOzLBwwZmaWhQPGzMyycMCYmVkWDhgzM8vCAWNmZlk4\nYMzMLAsHjJmZZeGAMTOzLBwwZmaWhQPGzMyycMCYmVkWDhgzM8vCAWNmZlk4YMzMLAsHjJmZZeGA\nMTOzLBwwZmaWhQPGzMyycMCYmVkWDhgzM8vCAWNmZlk4YMzMLAsHjJmZZeGAMTOzLLIFjKTxklZK\neqGo1l3STEk16b1bqkvSjZKWSlokqW/ROiNT+xpJI4vqx0hanNa5UZJy7YuZmW29nEcwdwCDtqiN\nBWZHRB9gdpoHOB3ok14XALdAIZCAK4H+wLHAlQ2hlNpcULTelp9lZmZllC1gIuIPwOotykOACWl6\nAnB2UX1iFMwF9pC0L3AaMDMiVkfEGmAmMCgt2y0inoqIACYWbcvMzDqAbX0NZp+IeAMgve+d6vsB\nrxe1q0215uq1jdRbJGmcpJAUK1asaNVOmJlZyzrKRf7Grp9EK+otiohxEaGIUI8ePbaii2ZmtjW2\ndcC8mU5vkd5Xpnot0LOoXTWwooV6dSN1MzPrILZ1wEwDGkaCjQSmFtVHpNFkA4B30ym0h4GBkrql\ni/sDgYfTsrWSBqTRYyOKtmVmZh1AVa4NS7oHOBHYU1IthdFg1wL3SRoDvAYMTc2nA2cAS4F1wGiA\niFgt6Rpgfmp3dUQ0DBy4iMJItV2AGellZmYdRLaAiYjhTSw6pZG2AVzcxHbGA+MbqS8ADm9LH83M\nLJ+OcpHfzMw6GQeMmZll4YAxM7MsHDBmZpaFA8bMzLJwwJiZWRYOGDMzy8IBY2ZmWThgzMwsCweM\nmZll4YAxM7MsHDBmZpaFA8bMzLJwwJiZWRYOGDMzy8IBY2ZmWThgzMwsCweMmZll4YAxM7MsHDBm\nZpaFA8bMzLJwwJiZWRYOGDMzy8IBY2ZmWThgzMwsCweMmZllsd0HjKRBkl6WtFTS2HL3x8zMCqrK\n3YG2kFQJ3AScCtQC8yVNi4gl5e3Zjum1q48odxc6jP1/uLjcXbAi/tn80Lb82dzej2COBZZGxLKI\n2AhMAoaUuU9mZsZ2fgQD7Ae8XjRfC/RvbgVJ44Ar0+w6SS/l6dqO5++gB7Ci3P3oEK5UuXtgRfyz\nWaR9fjb/rpRG23vANPZNRXMrRMQ4YFyOzuzoJEVE9Ch3P8y25J/N8tjeT5HVAj2L5qvxv1LMzDqE\n7T1g5gN9JB0gqQswDJhW5j6ZmRnb+SmyiKiT9A3gYaASGB8RL5a5Wzuyq8rdAbMm+GezDBTR7CUL\nMzOzVtneT5GZmVkH5YAxM7MsHDBmZpaFA8bMzLJwwJiZWRYOGDMzy8IBY23mRyZYRyVpvKSVkl4o\nd192RA4Ya5OiRyacDhwKDJd0aHl7ZbbZHcCgcndiR+WAsbbyIxOsw4qIPwCry92PHZUDxtqqsUcm\n7FemvphZB+KAsbba6kcmmNmOwQFjbeVHJphZoxww1lZ+ZIKZNcoBY20SEXVAwyMTXgLu8yMTrKOQ\ndA/wFPAZSbWSxpS7TzsS367fzMyy8BGMmZll4YAxM7MsHDBmZpaFA8bMzLJwwJiZWRYOGDMzy8IB\nYx2SpFcl/VHS85JekDSs3H3akqQLJV2aadtzJC2T9Fx63ZvhM3pJCkn3b1GfkOqHp/nfSPp8K7bf\nQ9Jj7dVf2/7472CsQ5L0KvCliHhB0tHAk0DPiFhV1KYyIurL1cecJM0BfhYRD2b8jF7AQuAt4HMR\nsUbSp4DngJ2BQRHh56hYq/kIxjq8iHgWWAuMlvSQpDslLQSOkNRb0mxJiyQ9I2nzsz8kfU7S4+ko\n6HlJA1P9M5JmSJqf6qNTvauk+yUtSfX7ito/VXQ09Z1UHyfpZ2l6lKRHJN0r6UVJT0j6H2lZF0m/\nkvTfqT+/lDS5Nd9F2tZ1kualI5s7Uyggabd0tDEvfR+/SM/rafbrBe4Dhqf5ocDvgLqiz5wj6Utp\n+gJJL6XPXiTpYEkVkm4uOuJ8IrXtJan4HwQh6Xvpe18m6ctFy76c1n82tYmG/bLtWET45VeHewGv\nAoen6ZOAvwLfAt4DDipq9zQwJk0fCqwC9gK6A38BjkvLKoFuQBWFf7UfnOq7Ai8DBwP/C5hVtO1u\n6f0XwL83Uh9H4SgDYBSwhsJRFsCvgR+l6X8FHkqfvTMwF5jcwv7PAZZROJp4Dhid6j8AflDU7idF\nn/Mb4Nw0XQHcA3ytmc/olb6vA4G5RZ97+Bbf/xwKR5MA7xbt405AV+Bo4L+Bii2+n17AqqLPC+Ab\nafp4YHma3ht4G+iT5i9NbT9V7p9Dv9r2qsKs45osaQOFcPkyhefMPB4RfwKQtCtwFHA7QEQskfQc\nMADYBCyJiCfTsnpgTXra5iHAJGnzkwZ2SrXngYMl3UThl+rv0/I/AD9LN/N8LL0a80RENDwbZy5w\napo+CbgzCvdtq0v3xyrlmsY34+OnyAYDu0k6p6jvzxctO1bSv6X5rhTudt2siFgm6X1JZwBdo3Ba\nsqnmjwK3S5oK/D6tu4xCgN8m6VGgudN6k9L7XKCHpJ0p/Pd6JiJq0rLxwM9b6rd1fA4Y68jOiaJr\nAJJGUTiC2VxqYr1oZpko/Kv6qEYXSocAp1B4BPR/SjoiIn4r6SlgIDAWOA/4aiOrbyiarufD/79E\n+z0jR8C/RMSjTSw7OyKWtWK7E4CJFI7KmvOPwGeBk4HHJF0YETMkHQacSOG7+4mkvk2svwEKgZ9C\nrIr2/X6sA/E1GNtuRcRfKZw+Ggkg6WDgSAqnzZ4EDpX0ubSsUlI3CqfD1kk6t2E76TrCbpKqgfqI\n+B2F0zR7Ad0l9Qb+EhF3AFdReEz01ngM+KqkqvQv9n9q9U4XHoXwbUm7pL7vmkKxYdnYhusukvaU\ndECJ270P+Blwd1MNJFUBB0bEvIi4FngEOFrSXsAuEfEQhQB+l8Jpt1LNBY5J3zMUTjdaJ+CAse3d\nP1P45b0I+D8UrkG8FRGrKfxr++dp2ULgmHSa6ixgWLpI/SJwM9AFOAJ4StLzwDzgxxGxAvgKsFjS\ns8B/AZdsZR9vBd4AXqRw+mghhV/CrXEthVNi89N+PU7h9B4UrlHVA89LWkzhuk9Jj6+OiPci4tqI\nWNNMs0rgDkmL03e0L/C/KTxwblaqLQJmUAiNkkTEm8CFwO/TAIFdgA+AdaVuwzomD1M22wYk7RoR\nayXtROFI4/6I+E25+9VRNHw/aXo0hYEbJ5S5W9ZGvgZjtm3MSuGyMzALuKO83elwvilpKIXfSauB\nr5W5P9YOfARjViaSzqfwNNAtjYqI59rxc6YB+29Rfi0iBrfXZ5g1xgFjZmZZ+CK/mZll4YAxM7Ms\nHDBmZpaFA8bMzLL4//5h0Rz+0Xn1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2639df31ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Processing_Fee_Missing'] = train['Processing_Fee'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "sns.countplot(x=\"Processing_Fee_Missing\", hue=\"Disbursed\",data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于Loan_Amount_Submitted和Loan_Tenure_Submitted，是否缺失显然没什么太大影响。不如直接用中值填补。而Interest_Rate和Processing_Fee大半都是缺失，还是删除该特征吧。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除missing列\n",
    "train=train.drop(['Loan_Amount_Submitted_Missing','Interest_Rate_Missing','Loan_Tenure_Submitted_Missing','Processing_Fee_Missing'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender                   0\n",
      "Monthly_Income           0\n",
      "Age                      0\n",
      "Loan_Amount_Applied      0\n",
      "Loan_Tenure_Applied      0\n",
      "Existing_EMI             0\n",
      "Mobile_Verified          0\n",
      "Loan_Amount_Submitted    0\n",
      "Loan_Tenure_Submitted    0\n",
      "Filled_Form              0\n",
      "Var4                     0\n",
      "Disbursed                0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "train=train.drop(['Interest_Rate','Processing_Fee'],axis=1)\n",
    "#中值填补\n",
    "medians = train.median() \n",
    "train = train.fillna(medians)\n",
    "\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 87019 entries, 0 to 87019\n",
      "Data columns (total 12 columns):\n",
      "Gender                   87019 non-null int64\n",
      "Monthly_Income           87019 non-null int64\n",
      "Age                      87019 non-null int64\n",
      "Loan_Amount_Applied      87019 non-null float64\n",
      "Loan_Tenure_Applied      87019 non-null float64\n",
      "Existing_EMI             87019 non-null float64\n",
      "Mobile_Verified          87019 non-null int64\n",
      "Loan_Amount_Submitted    87019 non-null float64\n",
      "Loan_Tenure_Submitted    87019 non-null float64\n",
      "Filled_Form              87019 non-null int64\n",
      "Var4                     87019 non-null int64\n",
      "Disbursed                87019 non-null float64\n",
      "dtypes: float64(6), int64(6)\n",
      "memory usage: 11.1 MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读数据\n",
    "y_train=train['Disbursed']\n",
    "X_train=train.drop('Disbursed',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# prepare cross validation\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 首先调参n_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "#直接调用xgboost内嵌的交叉验证（cv），可对连续的n_estimators参数进行快速交叉验证\n",
    "#而GridSearchCV只能对有限个参数进行交叉验证\n",
    "def modelfit(alg, X_train, y_train, cv_folds=None, early_stopping_rounds=10):\n",
    "    xgb_param = alg.get_xgb_params()\n",
    "    \n",
    "    #直接调用xgboost，而非sklarn的wrapper类\n",
    "    xgtrain = xgb.DMatrix(X_train, label = y_train)\n",
    "        \n",
    "    cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], folds =cv_folds,\n",
    "             metrics='logloss', early_stopping_rounds=early_stopping_rounds)\n",
    "  \n",
    "    cvresult.to_csv('1_nestimators.csv', index_label = 'n_estimators')\n",
    "    \n",
    "    #最佳参数n_estimators\n",
    "    n_estimators = cvresult.shape[0]\n",
    "    \n",
    "    # 采用交叉验证得到的最佳参数n_estimators，训练模型\n",
    "    alg.set_params(n_estimators = n_estimators)\n",
    "    alg.fit(X_train, y_train, eval_metric='logloss')\n",
    "        \n",
    "    #Predict training set:\n",
    "    #train_predprob = alg.predict_proba(X_train)\n",
    "    #logloss = log_loss(y_train, train_predprob)\n",
    "\n",
    "   #Print model report:\n",
    "   # print (\"logloss of train :\" )\n",
    "   # print logloss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "#params = {\"objective\": \"multi:softprob\", \"eval_metric\":\"mlogloss\", \"num_class\": 9}\n",
    "xgb1 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=1000,  #数值大没关系，cv会自动返回合适的n_estimators\n",
    "        max_depth=5,\n",
    "        min_child_weight=1,\n",
    "        gamma=0,\n",
    "        subsample=0.3,\n",
    "        colsample_bytree=0.8,\n",
    "        colsample_bylevel=0.7,\n",
    "        objective= 'binary:logistic')\n",
    "\n",
    "modelfit(xgb1, X_train, y_train, cv_folds = kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PjbocEZHI6RfXKUoGjwdgz8YlEVciIpIbFBIpKoYHv/WzbbrQn4gIKCT2M2RU8FuJsrpV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\niIikpZAQEZG0FBIiIpKWQkJERNL6/57sghBa7EcnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2639a0286a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvresult = pd.DataFrame.from_csv('1_nestimators.csv')\n",
    "\n",
    "# plot\n",
    "test_means = cvresult['test-logloss-mean']\n",
    "test_stds = cvresult['test-logloss-std'] \n",
    "        \n",
    "train_means = cvresult['train-logloss-mean']\n",
    "train_stds = cvresult['train-logloss-std'] \n",
    "\n",
    "x_axis = range(0, cvresult.shape[0])\n",
    "        \n",
    "pyplot.errorbar(x_axis, test_means, yerr=test_stds ,label='Test')\n",
    "pyplot.errorbar(x_axis, train_means, yerr=train_stds ,label='Train')\n",
    "pyplot.title(\"XGBoost n_estimators vs Log Loss\")\n",
    "pyplot.xlabel( 'n_estimators' )\n",
    "pyplot.ylabel( 'Log Loss' )\n",
    "pyplot.savefig( 'n_estimators4_1.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WEQkJCrAKmYOILiukaPtqikvL/a5GREQOQQFWIak95Qnp9GE9t0/7ltzCEr8rEhGRg1CA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2639d907fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvresult = pd.DataFrame.from_csv('1_nestimators.csv')\n",
    "cvresult = cvresult.iloc[20:]    \n",
    "# plot\n",
    "test_means = cvresult['test-logloss-mean']\n",
    "test_stds = cvresult['test-logloss-std'] \n",
    "        \n",
    "train_means = cvresult['train-logloss-mean']\n",
    "train_stds = cvresult['train-logloss-std'] \n",
    "\n",
    "x_axis = range(20, cvresult.shape[0]+20)\n",
    "\n",
    "fig = pyplot.figure(figsize=(10, 10), dpi=50)\n",
    "pyplot.errorbar(x_axis, test_means, yerr=test_stds ,label='Test')\n",
    "pyplot.errorbar(x_axis, train_means, yerr=train_stds ,label='Train')\n",
    "pyplot.title(\"XGBoost n_estimators vs Log Loss\")\n",
    "pyplot.xlabel( 'n_estimators' )\n",
    "pyplot.ylabel( 'Log Loss' )\n",
    "pyplot.savefig( 'n_estimators4_1.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 然后粗调树的参数Max Depth和Min Child Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': range(3, 10, 2), 'min_child_weight': range(1, 6, 2)}"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#max_depth 建议3-10， min_child_weight=1／sqrt(ratio_rare_event) =5.5\n",
    "max_depth = range(3,10,2)\n",
    "min_child_weight = range(1,6,2)\n",
    "param_test2_1 = dict(max_depth=max_depth, min_child_weight=min_child_weight)\n",
    "param_test2_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([mean: -0.06535, std: 0.00062, params: {'max_depth': 3, 'min_child_weight': 1},\n",
       "  mean: -0.06539, std: 0.00063, params: {'max_depth': 3, 'min_child_weight': 3},\n",
       "  mean: -0.06537, std: 0.00070, params: {'max_depth': 3, 'min_child_weight': 5},\n",
       "  mean: -0.06519, std: 0.00105, params: {'max_depth': 5, 'min_child_weight': 1},\n",
       "  mean: -0.06507, std: 0.00112, params: {'max_depth': 5, 'min_child_weight': 3},\n",
       "  mean: -0.06535, std: 0.00114, params: {'max_depth': 5, 'min_child_weight': 5},\n",
       "  mean: -0.06566, std: 0.00133, params: {'max_depth': 7, 'min_child_weight': 1},\n",
       "  mean: -0.06520, std: 0.00096, params: {'max_depth': 7, 'min_child_weight': 3},\n",
       "  mean: -0.06550, std: 0.00124, params: {'max_depth': 7, 'min_child_weight': 5},\n",
       "  mean: -0.06640, std: 0.00133, params: {'max_depth': 9, 'min_child_weight': 1},\n",
       "  mean: -0.06582, std: 0.00134, params: {'max_depth': 9, 'min_child_weight': 3},\n",
       "  mean: -0.06534, std: 0.00124, params: {'max_depth': 9, 'min_child_weight': 5}],\n",
       " {'max_depth': 5, 'min_child_weight': 3},\n",
       " -0.065073037801317235)"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb2_1 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=94,  #第一轮参数调整得到的n_estimators最优值\n",
    "        max_depth=5,\n",
    "        min_child_weight=1,\n",
    "        gamma=0,\n",
    "        subsample=0.3,\n",
    "        colsample_bytree=0.8,\n",
    "        colsample_bylevel = 0.7,\n",
    "        objective= 'binary:logistic',\n",
    "        seed=3)\n",
    "\n",
    "\n",
    "gsearch2_1 = GridSearchCV(xgb2_1, param_grid = param_test2_1, scoring='neg_log_loss',n_jobs=-1, cv=kfold)\n",
    "gsearch2_1.fit(X_train , y_train)\n",
    "#查看一下各参数组合的结果分布\n",
    "gsearch2_1.grid_scores_, gsearch2_1.best_params_,     gsearch2_1.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([  5.89021525,   6.20771375,   6.41922603,   8.15015402,\n",
       "          7.97470636,   7.99205194,   9.86923795,  10.18040009,\n",
       "         10.37566266,  11.74382086,  11.21302428,  10.43773403]),\n",
       " 'mean_score_time': array([ 0.07579746,  0.07101092,  0.07041173,  0.10771346,  0.10392227,\n",
       "         0.10312467,  0.14621005,  0.15259209,  0.12885652,  0.16296487,\n",
       "         0.14581065,  0.13863101]),\n",
       " 'mean_test_score': array([-0.06534787, -0.06538929, -0.06537336, -0.06518772, -0.06507304,\n",
       "        -0.06535478, -0.06565835, -0.06520059, -0.06549823, -0.06640157,\n",
       "        -0.06581739, -0.06534483]),\n",
       " 'mean_train_score': array([-0.06381213, -0.06385559, -0.06390487, -0.06061859, -0.06127475,\n",
       "        -0.06171088, -0.05667365, -0.05878366, -0.05979418, -0.05270298,\n",
       "        -0.05667196, -0.05842415]),\n",
       " 'param_max_depth': masked_array(data = [3 3 3 5 5 5 7 7 7 9 9 9],\n",
       "              mask = [False False False False False False False False False False False False],\n",
       "        fill_value = ?),\n",
       " 'param_min_child_weight': masked_array(data = [1 3 5 1 3 5 1 3 5 1 3 5],\n",
       "              mask = [False False False False False False False False False False False False],\n",
       "        fill_value = ?),\n",
       " 'params': [{'max_depth': 3, 'min_child_weight': 1},\n",
       "  {'max_depth': 3, 'min_child_weight': 3},\n",
       "  {'max_depth': 3, 'min_child_weight': 5},\n",
       "  {'max_depth': 5, 'min_child_weight': 1},\n",
       "  {'max_depth': 5, 'min_child_weight': 3},\n",
       "  {'max_depth': 5, 'min_child_weight': 5},\n",
       "  {'max_depth': 7, 'min_child_weight': 1},\n",
       "  {'max_depth': 7, 'min_child_weight': 3},\n",
       "  {'max_depth': 7, 'min_child_weight': 5},\n",
       "  {'max_depth': 9, 'min_child_weight': 1},\n",
       "  {'max_depth': 9, 'min_child_weight': 3},\n",
       "  {'max_depth': 9, 'min_child_weight': 5}],\n",
       " 'rank_test_score': array([ 5,  8,  7,  2,  1,  6, 10,  3,  9, 12, 11,  4]),\n",
       " 'split0_test_score': array([-0.06507806, -0.06506779, -0.06511252, -0.0643683 , -0.06427242,\n",
       "        -0.06460116, -0.06497937, -0.06452638, -0.06487147, -0.06572156,\n",
       "        -0.06497143, -0.0642205 ]),\n",
       " 'split0_train_score': array([-0.0638382 , -0.06387693, -0.06396454, -0.06094456, -0.06159209,\n",
       "        -0.06200472, -0.05678362, -0.05896343, -0.06005937, -0.05322296,\n",
       "        -0.05720241, -0.05868075]),\n",
       " 'split1_test_score': array([-0.06633624, -0.06631503, -0.06631   , -0.06704788, -0.06704536,\n",
       "        -0.06702338, -0.06778451, -0.06669975, -0.06747682, -0.06851723,\n",
       "        -0.06797385, -0.06738933]),\n",
       " 'split1_train_score': array([-0.06371397, -0.06377508, -0.06376266, -0.06020726, -0.06073971,\n",
       "        -0.0611906 , -0.05627804, -0.05821689, -0.05935559, -0.05220606,\n",
       "        -0.05654435, -0.05807285]),\n",
       " 'split2_test_score': array([-0.06577406, -0.06597633, -0.06608815, -0.06563138, -0.06561478,\n",
       "        -0.06638283, -0.06663559, -0.06597148, -0.06642056, -0.06739581,\n",
       "        -0.0667939 , -0.06613906]),\n",
       " 'split2_train_score': array([-0.06374352, -0.06360771, -0.0637046 , -0.06052251, -0.06118965,\n",
       "        -0.06155581, -0.05651254, -0.05869802, -0.05960131, -0.0525681 ,\n",
       "        -0.05629968, -0.05822563]),\n",
       " 'split3_test_score': array([-0.0648311 , -0.06481263, -0.06478578, -0.06457807, -0.06411404,\n",
       "        -0.0647231 , -0.06444123, -0.0644851 , -0.06434654, -0.06526428,\n",
       "        -0.06488614, -0.06469863]),\n",
       " 'split3_train_score': array([-0.06380059, -0.06393787, -0.06401286, -0.0605525 , -0.06145372,\n",
       "        -0.06189746, -0.05684321, -0.05898477, -0.05994351, -0.05267789,\n",
       "        -0.0569464 , -0.05843502]),\n",
       " 'split4_test_score': array([-0.06471984, -0.06477461, -0.06457031, -0.06431292, -0.06431854,\n",
       "        -0.06404336, -0.06445096, -0.06432017, -0.06437565, -0.06510886,\n",
       "        -0.06446153, -0.06427659]),\n",
       " 'split4_train_score': array([-0.06396439, -0.06408036, -0.06407969, -0.06086609, -0.06139859,\n",
       "        -0.0619058 , -0.05695083, -0.05905519, -0.06001114, -0.0528399 ,\n",
       "        -0.05636696, -0.05870652]),\n",
       " 'std_fit_time': array([ 0.21514478,  0.15312943,  0.09198504,  0.07031732,  0.06551631,\n",
       "         0.07656842,  0.10214268,  0.36402642,  0.33860538,  0.13038712,\n",
       "         0.11764641,  0.6573742 ]),\n",
       " 'std_score_time': array([ 0.01022943,  0.00277816,  0.00264665,  0.00295868,  0.00317875,\n",
       "         0.0037101 ,  0.02393284,  0.02558242,  0.00582922,  0.01232155,\n",
       "         0.00263059,  0.05084181]),\n",
       " 'std_test_score': array([ 0.00061527,  0.00063488,  0.00069948,  0.00104551,  0.0011237 ,\n",
       "         0.00114262,  0.00133235,  0.00095743,  0.00124458,  0.00133352,\n",
       "         0.00134351,  0.00123561]),\n",
       " 'std_train_score': array([  8.75679732e-05,   1.58540311e-04,   1.45682762e-04,\n",
       "          2.64689085e-04,   2.97182672e-04,   3.01293298e-04,\n",
       "          2.45004636e-04,   3.08235918e-04,   2.71756251e-04,\n",
       "          3.33131390e-04,   3.47732484e-04,   2.48402067e-04])}"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#看一下交叉验证结果？\n",
    "gsearch2_1.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: -0.065073 using {'max_depth': 5, 'min_child_weight': 3}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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AJ+90LboejqZGUODfafbKK1h8fUh+512j/s6q1To/m0ajqVQqVXCUUsOUUkeVUieUUn8q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2639df07518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图展现一下，不然上面的数据看不清楚\n",
    "# 先提取结果\n",
    "print(\"Best: %f using %s\" % (gsearch2_1.best_score_, gsearch2_1.best_params_))\n",
    "test_means = gsearch2_1.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = gsearch2_1.cv_results_[ 'std_test_score' ]\n",
    "train_means = gsearch2_1.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = gsearch2_1.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "pd.DataFrame(gsearch2_1.cv_results_).to_csv('my_preds_maxdepth_min_child_weights_1.csv')\n",
    "\n",
    "# 画出来\n",
    "test_scores = np.array(test_means).reshape(len(max_depth), len(min_child_weight))\n",
    "train_scores = np.array(train_means).reshape(len(max_depth), len(min_child_weight))\n",
    "\n",
    "for i, value in enumerate(max_depth):\n",
    "    pyplot.plot(min_child_weight, -test_scores[i], label= 'test_max_depth:'   + str(value))\n",
    "#for i, value in enumerate(min_child_weight):\n",
    "#    pyplot.plot(max_depth, train_scores[i], label= 'train_min_child_weight:'   + str(value))\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'min_child_weight' )                                                                                                      \n",
    "pyplot.ylabel( 'Log Loss' )\n",
    "pyplot.savefig('max_depth_vs_min_child_weght_1.png' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "显而易见，根据此次粗略的调参，最大树深度为5且最小叶子节点样本权重和为3时损失最小。接下来第二轮细调。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 细调树的参数Max Depth和Min Child Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': [4, 5, 6], 'min_child_weight': [2, 3, 4]}"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_depth = [4,5,6]\n",
    "min_child_weight = [2,3,4]\n",
    "param_test2_2 = dict(max_depth=max_depth, min_child_weight=min_child_weight)\n",
    "param_test2_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([mean: -0.06529, std: 0.00092, params: {'max_depth': 4, 'min_child_weight': 2},\n",
       "  mean: -0.06521, std: 0.00085, params: {'max_depth': 4, 'min_child_weight': 3},\n",
       "  mean: -0.06532, std: 0.00096, params: {'max_depth': 4, 'min_child_weight': 4},\n",
       "  mean: -0.06497, std: 0.00111, params: {'max_depth': 5, 'min_child_weight': 2},\n",
       "  mean: -0.06507, std: 0.00112, params: {'max_depth': 5, 'min_child_weight': 3},\n",
       "  mean: -0.06531, std: 0.00095, params: {'max_depth': 5, 'min_child_weight': 4},\n",
       "  mean: -0.06518, std: 0.00099, params: {'max_depth': 6, 'min_child_weight': 2},\n",
       "  mean: -0.06523, std: 0.00116, params: {'max_depth': 6, 'min_child_weight': 3},\n",
       "  mean: -0.06528, std: 0.00110, params: {'max_depth': 6, 'min_child_weight': 4}],\n",
       " {'max_depth': 5, 'min_child_weight': 2},\n",
       " -0.064971792152727123)"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb2_2 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=94,  #第一轮参数调整得到的n_estimators最优值\n",
    "        max_depth=5,\n",
    "        min_child_weight=1,\n",
    "        gamma=0,\n",
    "        subsample=0.3,\n",
    "        colsample_bytree=0.8,\n",
    "        colsample_bylevel = 0.7,\n",
    "        objective= 'binary:logistic',\n",
    "        seed=3)\n",
    "\n",
    "\n",
    "gsearch2_2 = GridSearchCV(xgb2_2, param_grid = param_test2_2, scoring='neg_log_loss',n_jobs=-1, cv=kfold)\n",
    "gsearch2_2.fit(X_train , y_train)\n",
    "\n",
    "gsearch2_2.grid_scores_, gsearch2_2.best_params_,     gsearch2_2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: -0.064972 using {'max_depth': 5, 'min_child_weight': 2}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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L8bPH63arudNqHvXUGdulQxfnfpyY5JqE0ze6L2HBV/GDWlUFrH4Idi53Fiydvwrikq78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\nIuAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2639ea43550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图展现一下\n",
    "# 先提取结果\n",
    "print(\"Best: %f using %s\" % (gsearch2_2.best_score_, gsearch2_2.best_params_))\n",
    "test_means = gsearch2_2.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = gsearch2_2.cv_results_[ 'std_test_score' ]\n",
    "train_means = gsearch2_2.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = gsearch2_2.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "pd.DataFrame(gsearch2_2.cv_results_).to_csv('my_preds_maxdepth_min_child_weights_1.csv')\n",
    "\n",
    "# 画出来\n",
    "test_scores = np.array(test_means).reshape(len(max_depth), len(min_child_weight))\n",
    "train_scores = np.array(train_means).reshape(len(max_depth), len(min_child_weight))\n",
    "\n",
    "for i, value in enumerate(max_depth):\n",
    "    pyplot.plot(min_child_weight, -test_scores[i], label= 'test_max_depth:'   + str(value))\n",
    "#for i, value in enumerate(min_child_weight):\n",
    "#    pyplot.plot(max_depth, train_scores[i], label= 'train_min_child_weight:'   + str(value))\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'min_child_weight' )                                                                                                      \n",
    "pyplot.ylabel( 'Log Loss' )\n",
    "pyplot.savefig('max_depth_vs_min_child_weght_1.png' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 由于看图，最佳可能在2还要往左，所以再调一次……"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([mean: -0.06513, std: 0.00120, params: {'max_depth': 5, 'min_child_weight': 1.5},\n",
       "  mean: -0.06497, std: 0.00111, params: {'max_depth': 5, 'min_child_weight': 2},\n",
       "  mean: -0.06502, std: 0.00107, params: {'max_depth': 5, 'min_child_weight': 2.5}],\n",
       " {'max_depth': 5, 'min_child_weight': 2},\n",
       " -0.064971792152727123)"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_depth = [5]\n",
    "min_child_weight = [1.5,2,2.5]\n",
    "param_test2_3 = dict(max_depth=max_depth, min_child_weight=min_child_weight)\n",
    "xgb2_3 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=94,  #第一轮参数调整得到的n_estimators最优值\n",
    "        max_depth=5,\n",
    "        min_child_weight=1,\n",
    "        gamma=0,\n",
    "        subsample=0.3,\n",
    "        colsample_bytree=0.8,\n",
    "        colsample_bylevel = 0.7,\n",
    "        objective= 'binary:logistic',\n",
    "        seed=3)\n",
    "\n",
    "\n",
    "gsearch2_3 = GridSearchCV(xgb2_3, param_grid = param_test2_3, scoring='neg_log_loss',n_jobs=-1, cv=kfold)\n",
    "gsearch2_3.fit(X_train , y_train)\n",
    "\n",
    "gsearch2_3.grid_scores_, gsearch2_3.best_params_,     gsearch2_3.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: -0.064972 using {'max_depth': 5, 'min_child_weight': 2}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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bdx/h/95NJS/P7hcZU1UEsh7RJGAQsAJYBQxW1UmqelBVxxRzaA8gXVU3qGo2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\ntPmW2z8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2639a028cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图展现一下\n",
    "# 先提取结果\n",
    "print(\"Best: %f using %s\" % (gsearch2_3.best_score_, gsearch2_3.best_params_))\n",
    "test_means = gsearch2_3.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = gsearch2_3.cv_results_[ 'std_test_score' ]\n",
    "train_means = gsearch2_3.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = gsearch2_3.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "pd.DataFrame(gsearch2_3.cv_results_).to_csv('my_preds_maxdepth_min_child_weights_1.csv')\n",
    "\n",
    "# 画出来\n",
    "test_scores = np.array(test_means).reshape(len(max_depth), len(min_child_weight))\n",
    "train_scores = np.array(train_means).reshape(len(max_depth), len(min_child_weight))\n",
    "\n",
    "for i, value in enumerate(max_depth):\n",
    "    pyplot.plot(min_child_weight, -test_scores[i], label= 'test_max_depth:'   + str(value))\n",
    "#for i, value in enumerate(min_child_weight):\n",
    "#    pyplot.plot(max_depth, train_scores[i], label= 'train_min_child_weight:'   + str(value))\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'min_child_weight' )                                                                                                      \n",
    "pyplot.ylabel( 'Log Loss' )\n",
    "pyplot.savefig('max_depth_vs_min_child_weght_1.png' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可见最佳min child weight确实是2了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 现在调整树的参数：subsample 和 colsample_bytree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': [0.6, 0.7, 0.8, 0.9],\n",
       " 'subsample': [0.3, 0.4, 0.5, 0.6, 0.7, 0.8]}"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#还是一样的设置调参范围\n",
    "subsample = [i/10.0 for i in range(3,9)]\n",
    "colsample_bytree = [i/10.0 for i in range(6,10)]\n",
    "param_test3_1 = dict(subsample=subsample, colsample_bytree=colsample_bytree)\n",
    "param_test3_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([mean: -0.06531, std: 0.00105, params: {'colsample_bytree': 0.6, 'subsample': 0.3},\n",
       "  mean: -0.06509, std: 0.00098, params: {'colsample_bytree': 0.6, 'subsample': 0.4},\n",
       "  mean: -0.06501, std: 0.00104, params: {'colsample_bytree': 0.6, 'subsample': 0.5},\n",
       "  mean: -0.06508, std: 0.00103, params: {'colsample_bytree': 0.6, 'subsample': 0.6},\n",
       "  mean: -0.06498, std: 0.00103, params: {'colsample_bytree': 0.6, 'subsample': 0.7},\n",
       "  mean: -0.06504, std: 0.00104, params: {'colsample_bytree': 0.6, 'subsample': 0.8},\n",
       "  mean: -0.06529, std: 0.00108, params: {'colsample_bytree': 0.7, 'subsample': 0.3},\n",
       "  mean: -0.06514, std: 0.00114, params: {'colsample_bytree': 0.7, 'subsample': 0.4},\n",
       "  mean: -0.06500, std: 0.00099, params: {'colsample_bytree': 0.7, 'subsample': 0.5},\n",
       "  mean: -0.06481, std: 0.00101, params: {'colsample_bytree': 0.7, 'subsample': 0.6},\n",
       "  mean: -0.06487, std: 0.00104, params: {'colsample_bytree': 0.7, 'subsample': 0.7},\n",
       "  mean: -0.06484, std: 0.00086, params: {'colsample_bytree': 0.7, 'subsample': 0.8},\n",
       "  mean: -0.06497, std: 0.00111, params: {'colsample_bytree': 0.8, 'subsample': 0.3},\n",
       "  mean: -0.06478, std: 0.00092, params: {'colsample_bytree': 0.8, 'subsample': 0.4},\n",
       "  mean: -0.06484, std: 0.00090, params: {'colsample_bytree': 0.8, 'subsample': 0.5},\n",
       "  mean: -0.06480, std: 0.00095, params: {'colsample_bytree': 0.8, 'subsample': 0.6},\n",
       "  mean: -0.06487, std: 0.00103, params: {'colsample_bytree': 0.8, 'subsample': 0.7},\n",
       "  mean: -0.06471, std: 0.00082, params: {'colsample_bytree': 0.8, 'subsample': 0.8},\n",
       "  mean: -0.06518, std: 0.00118, params: {'colsample_bytree': 0.9, 'subsample': 0.3},\n",
       "  mean: -0.06504, std: 0.00094, params: {'colsample_bytree': 0.9, 'subsample': 0.4},\n",
       "  mean: -0.06484, std: 0.00103, params: {'colsample_bytree': 0.9, 'subsample': 0.5},\n",
       "  mean: -0.06475, std: 0.00107, params: {'colsample_bytree': 0.9, 'subsample': 0.6},\n",
       "  mean: -0.06469, std: 0.00107, params: {'colsample_bytree': 0.9, 'subsample': 0.7},\n",
       "  mean: -0.06464, std: 0.00103, params: {'colsample_bytree': 0.9, 'subsample': 0.8}],\n",
       " {'colsample_bytree': 0.9, 'subsample': 0.8},\n",
       " -0.064639765714254532)"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb3_1 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=94,  #第二轮参数调整得到的n_estimators最优值\n",
    "        max_depth=5,\n",
    "        min_child_weight=2,\n",
    "        gamma=0,\n",
    "        subsample=0.3,\n",
    "        colsample_bytree=0.8,\n",
    "        colsample_bylevel = 0.7,\n",
    "        objective= 'binary:logistic',\n",
    "        seed=3)\n",
    "\n",
    "\n",
    "gsearch3_1 = GridSearchCV(xgb3_1, param_grid = param_test3_1, scoring='neg_log_loss',n_jobs=-1, cv=kfold)\n",
    "gsearch3_1.fit(X_train , y_train)\n",
    "\n",
    "gsearch3_1.grid_scores_, gsearch3_1.best_params_,     gsearch3_1.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: -0.064640 using {'colsample_bytree': 0.9, 'subsample': 0.8}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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xj6UtK9KhgRuzhrYi0ySMXriPI5HxeTd2cILu02H4r1DBAza/DQsfhviIvPto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\nY4wxQWGBY4wxJij+P+JPh3Qd5fBUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2639ddfca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (gsearch3_1.best_score_, gsearch3_1.best_params_))\n",
    "test_means = gsearch3_1.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = gsearch3_1.cv_results_[ 'std_test_score' ]\n",
    "train_means = gsearch3_1.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = gsearch3_1.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "pd.DataFrame(gsearch3_1.cv_results_).to_csv('my_preds_subsampleh_colsample_bytree_1.csv')\n",
    "\n",
    "# plot results\n",
    "test_scores = np.array(test_means).reshape(len(colsample_bytree), len(subsample))\n",
    "train_scores = np.array(train_means).reshape(len(colsample_bytree), len(subsample))\n",
    "\n",
    "for i, value in enumerate(colsample_bytree):\n",
    "    pyplot.plot(subsample, -test_scores[i], label= 'test_colsample_bytree:'   + str(value))\n",
    "#for i, value in enumerate(min_child_weight):\n",
    "#    pyplot.plot(max_depth, train_scores[i], label= 'train_min_child_weight:'   + str(value))\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'subsample' )                                                                                                      \n",
    "pyplot.ylabel( 'Log Loss' )\n",
    "pyplot.savefig( 'subsample_vs_colsample_bytree1.png' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看趋势，似乎可以继续提高subsample和colsample以降低损失。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 调整正则化参数：reg_alpha 和reg_lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'reg_alpha': [1, 1.5, 2], 'reg_lambda': [1, 2, 4]}"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reg_alpha = [1e-3, 1e-2, 0.05, 0.1]    #default = 0\n",
    "#reg_lambda = [1e-3, 1e-2, 0.05, 0.1]   #default = 1\n",
    "\n",
    "reg_alpha = [ 1,1.5, 2]    #default = 0, 测试0.1,1，1.5，2\n",
    "reg_lambda = [1,2,4]      #default = 1，测试0.1， 0.5， 1，2\n",
    "\n",
    "param_test5_1 = dict(reg_alpha=reg_alpha, reg_lambda=reg_lambda)\n",
    "param_test5_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([mean: -0.06476, std: 0.00085, params: {'reg_alpha': 1, 'reg_lambda': 1},\n",
       "  mean: -0.06472, std: 0.00093, params: {'reg_alpha': 1, 'reg_lambda': 2},\n",
       "  mean: -0.06488, std: 0.00097, params: {'reg_alpha': 1, 'reg_lambda': 4},\n",
       "  mean: -0.06472, std: 0.00096, params: {'reg_alpha': 1.5, 'reg_lambda': 1},\n",
       "  mean: -0.06471, std: 0.00097, params: {'reg_alpha': 1.5, 'reg_lambda': 2},\n",
       "  mean: -0.06489, std: 0.00091, params: {'reg_alpha': 1.5, 'reg_lambda': 4},\n",
       "  mean: -0.06476, std: 0.00104, params: {'reg_alpha': 2, 'reg_lambda': 1},\n",
       "  mean: -0.06485, std: 0.00098, params: {'reg_alpha': 2, 'reg_lambda': 2},\n",
       "  mean: -0.06489, std: 0.00102, params: {'reg_alpha': 2, 'reg_lambda': 4}],\n",
       " {'reg_alpha': 1.5, 'reg_lambda': 2},\n",
       " -0.064708718339887186)"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb5_1 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=94,  #第二轮参数调整得到的n_estimators最优值\n",
    "        max_depth=5,\n",
    "        min_child_weight=2,\n",
    "        gamma=0,\n",
    "        subsample=0.8,\n",
    "        colsample_bytree=0.9,\n",
    "        colsample_bylevel = 0.7,\n",
    "        objective= 'binary:logistic',\n",
    "        seed=3)\n",
    "\n",
    "\n",
    "gsearch5_1 = GridSearchCV(xgb5_1, param_grid = param_test5_1, scoring='neg_log_loss',n_jobs=-1, cv=kfold)\n",
    "gsearch5_1.fit(X_train , y_train)\n",
    "\n",
    "gsearch5_1.grid_scores_, gsearch5_1.best_params_,     gsearch5_1.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: -0.064709 using {'reg_alpha': 1.5, 'reg_lambda': 2}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\zhuhaier\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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S08zfj/Lesr/xKeTB1H6NuadmWScGmzc4LEHZrikNB1ZglXF/oap7RWQssFVV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\ngXmGCv1XJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2639dda06d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (gsearch5_1.best_score_, gsearch5_1.best_params_))\n",
    "test_means = gsearch5_1.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = gsearch5_1.cv_results_[ 'std_test_score' ]\n",
    "train_means = gsearch5_1.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = gsearch5_1.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "pd.DataFrame(gsearch5_1.cv_results_).to_csv('my_preds_reg_alpha_reg_lambda_1.csv')\n",
    "\n",
    "# plot results\n",
    "test_scores = np.array(test_means).reshape(len(reg_alpha), len(reg_lambda))\n",
    "train_scores = np.array(train_means).reshape(len(reg_alpha), len(reg_lambda))\n",
    "\n",
    "#log_reg_alpha = [0,0,0,0]\n",
    "#for index in range(len(reg_alpha)):\n",
    "#   log_reg_alpha[index] = math.log10(reg_alpha[index])\n",
    "    \n",
    "for i, value in enumerate(reg_alpha):\n",
    "    pyplot.plot(reg_lambda, -test_scores[i], label= 'reg_alpha:'   + str(value))\n",
    "#for i, value in enumerate(min_child_weight):\n",
    "#    pyplot.plot(max_depth, train_scores[i], label= 'train_min_child_weight:'   + str(value))\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'reg_lambda' )                                                                                                      \n",
    "pyplot.ylabel( '-Log Loss' )\n",
    "pyplot.savefig( 'reg_alpha_vs_reg_lambda1.png' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "显然，最佳正则参数没有疑问。至此，完成了所有参数调优，如下。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 0 pruned nodes, max_depth=1\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 4 extra nodes, 0 pruned nodes, max_depth=2\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 4 extra nodes, 0 pruned nodes, max_depth=2\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 8 extra nodes, 0 pruned nodes, max_depth=4\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=4\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 8 extra nodes, 0 pruned nodes, max_depth=4\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 24 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 38 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 34 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 32 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 40 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 36 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 42 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 40 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 0 pruned nodes, max_depth=5\n",
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      "[23:58:05] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 34 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:05] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 44 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:05] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 36 extra nodes, 0 pruned nodes, max_depth=5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23:58:05] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:05] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 32 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 32 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 24 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 48 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 32 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 0 pruned nodes, max_depth=5\n",
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      "[23:58:07] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 0 pruned nodes, max_depth=5\n",
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      "[23:58:08] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 46 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:08] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:08] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 38 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:08] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:08] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:08] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 40 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:58:08] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 0 pruned nodes, max_depth=5\n"
     ]
    }
   ],
   "source": [
    "#用xgboost运行一下该参数组合\n",
    "param={\n",
    "    'eta':0.1,\n",
    "    'max_depth':5,\n",
    "    'min_child_weight':2,\n",
    "    'gamma':0,\n",
    "    'subsample':0.8,\n",
    "    'colsample_bytree':0.9,\n",
    "    'colsample_bylevel':0.7,\n",
    "    'objective':'binary:logistic',\n",
    "    'reg_alpha':1.5,\n",
    "    'reg_lambda':2,\n",
    "    'seed':3\n",
    "}\n",
    "num_round=94\n",
    "\n",
    "dtrain = xgb.DMatrix(X_train, label = y_train)\n",
    "bst = xgb.train(param, dtrain, num_round)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuary: 98.54%\n"
     ]
    }
   ],
   "source": [
    "#看一下准确率\n",
    "train_preds = bst.predict(dtrain)\n",
    "train_predictions = [round(value) for value in train_preds]\n",
    "y_train = dtrain.get_label()\n",
    "train_accuracy = accuracy_score(y_train, train_predictions)\n",
    "print (\"Train Accuary: %.2f%%\" % (train_accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test=pd.read_csv(dpath+'test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 37717 entries, 0 to 37716\n",
      "Data columns (total 24 columns):\n",
      "ID                       37717 non-null object\n",
      "Gender                   37717 non-null object\n",
      "City                     37319 non-null object\n",
      "Monthly_Income           37717 non-null int64\n",
      "DOB                      37717 non-null object\n",
      "Lead_Creation_Date       37717 non-null object\n",
      "Loan_Amount_Applied      37677 non-null float64\n",
      "Loan_Tenure_Applied      37677 non-null float64\n",
      "Existing_EMI             37677 non-null float64\n",
      "Employer_Name            37675 non-null object\n",
      "Salary_Account           32680 non-null object\n",
      "Mobile_Verified          37717 non-null object\n",
      "Var5                     37717 non-null int64\n",
      "Var1                     37717 non-null object\n",
      "Loan_Amount_Submitted    22795 non-null float64\n",
      "Loan_Tenure_Submitted    22795 non-null float64\n",
      "Interest_Rate            12110 non-null float64\n",
      "Processing_Fee           11971 non-null float64\n",
      "EMI_Loan_Submitted       12110 non-null float64\n",
      "Filled_Form              37717 non-null object\n",
      "Device_Type              37717 non-null object\n",
      "Var2                     37717 non-null object\n",
      "Source                   37717 non-null object\n",
      "Var4                     37717 non-null int64\n",
      "dtypes: float64(8), int64(3), object(13)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "显而易见，test.csv缺少关键的Disbursed标签，无法计算评价指标，所以略过。如有，则以与处理train特征同样的方式处理完特征后，套入模型中进行运算。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型特征重要性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XJ+nasHyrpJ9VYN8sSfPKr+lqu65du9KiRYttynr06EG9etHzqY499liWLl0K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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2639984d278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot feature importance using built-in function\n",
    "from xgboost import plot_importance\n",
    "plot_importance(bst)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "显然，月收入因素是影响最大的，并且远超其他因素。其他比较有影响力的因素包括已有的EMI贷款、年龄、申请以及提交的贷款金额等。影响力更小的因素包括是否填写表格、性别和手机号确认等。"
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